%! Author = vin
%! Date = 11/11/22

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	title        = {An Introduction to Convolutional Neural Networks},
	author       = {Keiron O'Shea and Ryan Nash},
	year         = 2015,
	journal      = {CoRR},
	volume       = {abs/1511.08458},
	url          = {http://arxiv.org/abs/1511.08458},
	archiveprefix = {arXiv},
	eprint       = {1511.08458},
	timestamp    = {Mon, 13 Aug 2018 16:46:52 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/OSheaN15},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{dropELM,
	title        = {DropELM: Fast Neural Network Regularization with Dropout and DropConnect},
	author       = {Alexandros Iosifidis and Anastasios Tefas and Ioannis Pitas},
	year         = 2005,
	journal      = {Neurocomputing},
	volume       = 162,
	pages        = {56--57}
}
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	title        = {Approximation by Superpositions of a Sigmoidal Function},
	author       = {G. Gybenko},
	year         = 1989,
	journal      = {Mathematics of Control, Signals and Systems},
	volume       = 2,
	pages        = {303--314}
}
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	title        = {Modern Information Retrieval},
	author       = {Baeza-Yates and Ricardo Ribeiro-Neto and Berthier},
	year         = 1999,
	publisher    = {NY: ACM Press},
	isbn         = {0-201-39829-X}
}
@article{sen_spec_overview,
	title        = {Diagnostic methods I: sensitivity, specificity, and other measures of accuracy},
	author       = {Karlijn Stralen and Vianda Stel1Johannes B. Reitsma and Friedo W. Dekker and CarmineZoccali and Kitty J.Jager},
	year         = 2009,
	journal      = {Kidney International},
	volume       = 75,
	pages        = {1257--1263},
	issue        = 12
}
@article{harmonic_mean_sen_spec,
	title        = {Orphelia: predicting genes in metagenomic sequencing reads},
	author       = {Katharina J. Hoff and Thomas Lingner and Peter Meinicke and Maike Tech},
	year         = 2009,
	journal      = {Nucleic Acids Research},
	volume       = 29
}
@article{melanoma_diagnosis,
	title        = {Melanoma: Assessment and Management},
	author       = {National Collaborating Centre for Cancer (UK)},
	year         = 2015,
	journal      = {London: National Institute for Health and Care Excellence (UK)},
	publisher    = {NICE Guideline}
}
@article{CNN_paper,
	title        = {Gradient-based learning applied to document recognition},
	author       = {Y. Lecun and L. Bottou and Y. Bengio and P. Haffner},
	year         = 1998,
	month        = {Nov},
	journal      = {Proceedings of the IEEE},
	volume       = 86,
	number       = 11,
	pages        = {2278--2324},
	doi          = {10.1109/5.726791},
	issn         = {0018-9219},
	keywords     = {optical character recognition;multilayer perceptrons;backpropagation;convolution;gradient-based learning;document recognition;multilayer neural networks;back-propagation;gradient based learning technique;complex decision surface synthesis;high-dimensional patterns;handwritten character recognition;handwritten digit recognition task;2D shape variability;document recognition systems;field extraction;segmentation recognition;language modeling;graph transformer networks;GTN;multimodule systems;performance measure minimization;cheque reading;convolutional neural network character recognizers;Neural networks;Pattern recognition;Machine learning;Optical character recognition software;Character recognition;Feature extraction;Multi-layer neural network;Optical computing;Hidden Markov models;Principal component analysis}
}
@misc{Cun88atheoretical,
	title        = {A Theoretical Framework for Back-Propagation},
	author       = {Yann Le Cun},
	year         = 1988
}
@book{perceptron,
	title        = {Neural Networks: A Systematic Introduction},
	author       = {Rojas, Raul},
	year         = 1996,
	publisher    = {Springer-Verlag},
	address      = {Berlin, Heidelberg},
	isbn         = {3-540-60505-3}
}
@article{Shin2016DeepCN,
	title        = {Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning},
	author       = {Hoo-Chang Shin and Holger Roth and Mingchen Gao and Le Lu and Ziyue Xu and Isabella Nogues and Jianhua Yao and Daniel J. Mollura and Ronald M. Summers},
	year         = 2016,
	journal      = {IEEE Transactions on Medical Imaging},
	volume       = 35,
	pages        = {1285--1298}
}
@article{transfer_learning_med,
	title        = {Understanding the Mechanisms of Deep Transfer Learning for Medical Images},
	author       = {Hariharan Ravishankar and Prasad Sudhakar and Rahul Venkataramani and Sheshadri Thiruvenkadam and Pavan Annangi and Narayanan Babu and Vivek Vaidya},
	year         = 2017,
	journal      = {CoRR},
	volume       = {abs/1704.06040},
	url          = {http://arxiv.org/abs/1704.06040},
	archiveprefix = {arXiv},
	eprint       = {1704.06040},
	timestamp    = {Mon, 13 Aug 2018 16:48:04 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/RavishankarSVTA17},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{abcde,
	title        = {Early diagnosis of cutaneous melanoma: revisiting the ABCD criteria},
	author       = {N.R Abbasi and H.M Shaw and D.S. Rigel and R.J. Friedman and W.H. McCarthy and I. Osman and A.W Kopf and D. Polsky},
	year         = 2004,
	journal      = {JAMA},
	volume       = 22,
	pages        = {2771--2776}
}
@article{3-point-checklist,
	title        = {Three-Point Checklist of Dermoscopy},
	author       = {Soyer Peter and Argenziano Giuseppe and Zalaudek Iris and Corona Rosamaria and Sera Francesco and Talamini Renato and Barbato Filomena and Baroni Adone and Cicale Lorenza and Di Stefani Alessandro and Farro Pietro and Rossiello Luigi and Ruocco Eleonora and Chimenti Sergio},
	year         = 2004,
	journal      = {Dermatology (Basel, Switzerland)},
	volume       = 208,
	pages        = {27--31}
}
@article{7-point-checklist,
	title        = {Using the 7-point checklist as a diagnostic aid for pigmented skin lesions in general practice: a diagnostic validation study},
	author       = {F.M. Walter, A.T. Prevost, J. Vasconcelos, et al.},
	year         = 2013,
	journal      = {Br J Gen Pract},
	volume       = 63,
	pages        = {345--353}
}
@article{kNNandSVM,
	title        = {Segmentation and Classification of Skin Lesions for Disease Diagnosis},
	author       = {R. Sumithra and Mahamad Suhil and D.S. Guru},
	year         = 2016,
	journal      = {CoRR},
	volume       = {abs/1609.03277},
	url          = {http://arxiv.org/abs/1609.03277},
	archiveprefix = {arXiv},
	eprint       = {1609.03277},
	timestamp    = {Mon, 13 Aug 2018 16:45:57 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/RSG16},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{PCA_skin,
	title        = {Computer-Aided Diagnosis of Skin Lesions Using Conventional Digital Photography: A Reliability and Feasibility Study},
	author       = {Wen-Yu Chang and Adam Huang and Chung-Yi Yang and Chien-Hung Lee and Yin-Chun Chen and Tian-Yau Wu and Gwo-Shing Chen},
	year         = 2013,
	journal      = {PLoS One},
	notes        = {PMCID:PMC3817186}
}
@article{data_aug,
	title        = {{Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks}},
	author       = {{Shin}, H.-C. and {Tenenholtz}, N.~A and {Rogers}, J.~K and {Schwarz}, C.~G and {Senjem}, M.~L and {Gunter}, J.~L and {Andriole}, K. and {Michalski}, M.},
	year         = 2018,
	month        = jul,
	journal      = {ArXiv e-prints},
	archiveprefix = {arXiv},
	eprint       = {1807.10225},
	primaryclass = {cs.CV},
	keywords     = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning},
	adsurl       = {http://adsabs.harvard.edu/abs/2018arXiv180710225S},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{data_aug_2,
	title        = {Understanding data augmentation for classification: when to warp?},
	author       = {Sebastien C. Wong and Adam Gatt and Victor Stamatescu and Mark D. McDonnell},
	year         = 2016,
	journal      = {CoRR},
	volume       = {abs/1609.08764},
	url          = {http://arxiv.org/abs/1609.08764},
	archiveprefix = {arXiv},
	eprint       = {1609.08764},
	timestamp    = {Mon, 13 Aug 2018 16:49:15 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/WongGSM16},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{DCGAN_diminishing,
	title        = {Controlled Synthesis of Dermoscopic Images via a New Color Labeled Generative Style Transfer Network to Enhance Melanoma Segmentation},
	author       = {Yucong Chi and Lei Bi and Jinman Jim and Dagan Feng and Ashnil Kumar},
	year         = 2018,
	journal      = {40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
	pages        = {PREPRINT}
}
@article{ieee_ashnil_class_imbalance,
	title        = {Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images},
	author       = {Euijoon Ahn and Jinman Kim and Lei Bi and Ashnil Kumar and Changyang Li and Michael Fulham and David Feng},
	year         = 2017,
	month        = {Nov},
	journal      = {IEEE Journal of Biomedical and Health Informatics},
	volume       = 21,
	number       = 6,
	pages        = {1685--1693},
	doi          = {10.1109/JBHI.2017.2653179},
	issn         = {2168-2194},
	keywords     = {Bayes methods;biomedical optical imaging;cancer;image reconstruction;image segmentation;medical image processing;optimisation;skin;unsupervised learning;saliency-based lesion segmentation;dermoscopic images;skin lesion segmentation;computer-aided diagnosis;melanoma;skin lesion oversegmentation;reconstruction errors;sparse representation model;Bayesian framework;unsupervised lesion segmentation methods;unsupervised saliency detection methods;saliency optimization algorithm;Lesions;Image segmentation;Skin;Image color analysis;Image reconstruction;Hair;Computational modeling;Computer-aided diagnosis (CAD);dermoscopic image;lesion segmentation;saliency detection;Algorithms;Dermoscopy;Hair;Humans;Image Interpretation, Computer-Assisted;Skin;Skin Neoplasms}
}
@article{diagnostic_accuracy_clinician,
	title        = {Pathologists{\textquoteright} diagnosis of invasive melanoma and melanocytic proliferations: observer accuracy and reproducibility study},
	author       = {Elmore, Joann G and Barnhill, Raymond L and Elder, David E and Longton, Gary M and Pepe, Margaret S and Reisch, Lisa M and Carney, Patricia A and Titus, Linda J and Nelson, Heidi D and Onega, Tracy and Tosteson, Anna N A and Weinstock, Martin A and Knezevich, Stevan R and Piepkorn, Michael W},
	year         = 2017,
	journal      = {BMJ},
	publisher    = {BMJ Publishing Group Ltd},
	volume       = 357,
	doi          = {10.1136/bmj.j2813},
	issn         = {0959-8138},
	url          = {https://www.bmj.com/content/357/bmj.j2813},
	eprint       = {https://www.bmj.com/content/357/bmj.j2813.full.pdf}
}
@article{logistic_and_other,
	title        = {A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions},
	author       = {Stephan Dreiseitl and Lucila Ohno-Machado and Harald Kittler and Staal Vinterbo and Holger Billhardt and Michael Binder},
	year         = 2001,
	journal      = {Journal of Biomedical Informatics},
	volume       = 34,
	number       = 1,
	pages        = {28--36},
	doi          = {https://doi.org/10.1006/jbin.2001.1004},
	issn         = {1532-0464},
	url          = {http://www.sciencedirect.com/science/article/pii/S1532046401910044},
	keywords     = {machine learning, decision support, image classification, neural networks, support vector machines.}
}
@article{recogniseMelanomaScreening,
	title        = {Time required for a complete skin examination with and without dermoscopy: A prospective, randomized multicenter study},
	author       = {Zalaudek I and Kittler H and Marghoob AA and et al},
	year         = 2008,
	journal      = {Archives of Dermatology},
	volume       = 144,
	number       = 4,
	pages        = {509--513},
	doi          = {10.1001/archderm.144.4.509},
	url          = {+ http://dx.doi.org/10.1001/archderm.144.4.509},
	eprint       = {/data/journals/derm/5129/dst70080\_509\_513.pdf}
}
@article{ir-search-health-professional,
	title        = {Expert Search Strategies: The Information Retrieval Practices of Healthcare Information Professionals},
	author       = {Russell-Rose, Tony and Chamberlain, Jon},
	year         = 2017,
	month        = {Oct},
	day          = {02},
	journal      = {JMIR Medical Informatics},
	volume       = 5,
	number       = 4,
	pages        = {e33},
	doi          = {10.2196/medinform.7680},
	issn         = {2291-9694},
	url          = {https://medinform.jmir.org/2017/4/e33/}
}
@inproceedings{ulmfit-2018,
	title        = {Universal Language Model Fine-tuning for Text Classification},
	author       = {Howard, Jeremy  and Ruder, Sebastian},
	year         = 2018,
	month        = jul,
	booktitle    = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics},
	address      = {Melbourne, Australia},
	pages        = {328--339},
	doi          = {10.18653/v1/P18-1031},
	url          = {https://www.aclweb.org/anthology/P18-1031},
	abstract     = {Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24{\%} on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100 times more data. We open-source our pretrained models and code.}
}
@inproceedings{orig-bert-2018,
	title        = {{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding},
	author       = {Devlin, Jacob  and Chang, Ming-Wei  and Lee, Kenton  and Toutanova, Kristina},
	year         = 2019,
	month        = jun,
	booktitle    = {Proceedings of the Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies},
	address      = {Minneapolis, MN},
	pages        = {4171--4186},
	doi          = {10.18653/v1/N19-1423},
	url          = {https://www.aclweb.org/anthology/N19-1423}
}
@article{elmo-2018,
	title        = {Deep contextualized word representations},
	author       = {Matthew Peters and Mark Neumann and Mohit Iyyer and Matt Gardner and Christopher Clark and Kenton Lee and Luke Zettlemoyer},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1802.05365},
	url          = {http://arxiv.org/abs/1802.05365},
	archiveprefix = {arXiv},
	eprint       = {1802.05365},
	timestamp    = {Mon, 13 Aug 2018 16:48:54 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1802-05365},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{google-transformer-2017,
	title        = {Attention Is All You Need},
	author       = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan Gomez and Lukasz Kaiser and Illia Polosukhin},
	year         = 2017,
	journal      = {CoRR},
	volume       = {abs/1706.03762},
	url          = {http://arxiv.org/abs/1706.03762},
	archiveprefix = {arXiv},
	eprint       = {1706.03762},
	timestamp    = {Mon, 13 Aug 2018 16:48:37 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/VaswaniSPUJGKP17},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{snorkel-2018,
	title        = {Snorkel DryBell: {A} Case Study in Deploying Weak Supervision at Industrial Scale},
	author       = {Stephen Bach and Daniel Rodriguez and Yintao Liu and Chong Luo and Haidong Shao and Cassandra Xia and Souvik Sen and Alexander Ratner and Braden Hancock and Houman Alborzi and Rahul Kuchhal and Christopher Re and Rob Malkin},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1812.00417},
	url          = {http://arxiv.org/abs/1812.00417},
	archiveprefix = {arXiv},
	eprint       = {1812.00417},
	timestamp    = {Tue, 01 Jan 2019 15:01:25 +0100},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1812-00417},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{CVAE-2018,
	title        = {On the Generation of Medical Question-Answer Pairs},
	author       = {Sheng Shen and Yaliang Li and Nan Du and Xian Wu and Yusheng Xie and Shen Ge and Tao Yang and Kai Wang and Xingzheng Liang and Wei Fan},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1811.00681},
	url          = {http://arxiv.org/abs/1811.00681},
	archiveprefix = {arXiv},
	eprint       = {1811.00681},
	timestamp    = {Thu, 22 Nov 2018 17:58:30 +0100},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1811-00681},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{squad_2016,
	title        = {SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
	author       = {Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
	year         = 2016,
	journal      = {CoRR},
	volume       = {abs/1606.05250},
	url          = {http://arxiv.org/abs/1606.05250},
	archiveprefix = {arXiv},
	eprint       = {1606.05250},
	timestamp    = {Mon, 13 Aug 2018 16:49:13 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/RajpurkarZLL16},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{narrative_qa_2017,
	title        = {The NarrativeQA Reading Comprehension Challenge},
	author       = {Tom{a}s Kociskyand Jonathan Schwarz and Phil Blunsom and Chris Dyer and Karl Hermann and G{a}bor Melis and Edward Grefenstette},
	year         = 2017,
	journal      = {CoRR},
	volume       = {abs/1712.07040},
	url          = {http://arxiv.org/abs/1712.07040},
	archiveprefix = {arXiv},
	eprint       = {1712.07040},
	timestamp    = {Mon, 13 Aug 2018 16:47:50 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1712-07040},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{cancer_impact_2006,
	title        = {Prostate cancer diagnosis: The impact on patients' mental health},
	author       = {Ida Korfage and Harry Koning and Monique Roobol and Fritz Schroder and Marie-Louise Essink-Bot},
	year         = 2006,
	journal      = {European Journal of Cancer},
	volume       = 42,
	number       = 2,
	pages        = {165--170},
	keywords     = {Prostatic neoplasms, Quality of life, Screening, PSA testing, Diagnosis, Mental health}
}
@article{logistic_regression_medical_2017,
	title        = {Quality of reporting of multivariable logistic regression models in Chinese clinical medical journals},
	author       = {Zhang, Ying-ying and Zhou, Xiao-bin and Wang, Qiu-zhen and Zhu, Xiao-yan},
	year         = 2017,
	journal      = {Medicine (Baltimore)},
	volume       = 96,
	number       = 21,
	url          = {https://journals.lww.com/md-journal/Fulltext/2017/05260/Quality\_of\_reporting\_of\_multivariable\_logistic.38.aspx}
}
@article{logistic_regression_benefit_2019,
	title        = {A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models},
	author       = {Evangelia Christodoulou and Jie Ma and Gary Collins and Ewout Steyerberg and Jan Verbakel and Ben Van Calster},
	year         = 2019,
	journal      = {J Clin Epidemiol},
	volume       = 110,
	pages        = {12--22},
	doi          = {https://doi.org/10.1016/j.jclinepi.2019.02.004},
	issn         = {0895-4356},
	url          = {http://www.sciencedirect.com/science/article/pii/S0895435618310813},
	keywords     = {Clinical prediction models, Logistic regression, Machine learning, AUC, Calibration, Reporting}
}
@article{question_entailment_2019,
	title        = {A Question-Entailment Approach to Question Answering},
	author       = {Asma {Ben Abacha} and Dina Demner{-}Fushman},
	year         = 2019,
	journal      = {CoRR},
	volume       = {abs/1901.08079},
	url          = {http://arxiv.org/abs/1901.08079},
	archiveprefix = {arXiv},
	eprint       = {1901.08079},
	timestamp    = {Sat, 02 Feb 2019 16:56:00 +0100},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1901-08079},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{radford2019language,
	title        = {Language Models are Unsupervised Multitask Learners},
	author       = {Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
	year         = 2019
}
@article{sunil_anand_transfer_learning_2017,
	title        = {What matters in a transferable neural network model for relation classification in the biomedical domain?},
	author       = {Sunil Sahu and Ashish Anand},
	year         = 2017,
	journal      = {CoRR},
	volume       = {abs/1708.03446},
	url          = {http://arxiv.org/abs/1708.03446},
	archiveprefix = {arXiv},
	eprint       = {1708.03446},
	timestamp    = {Mon, 13 Aug 2018 16:47:16 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1708-03446},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{lee_dernoncourt_2017,
	title        = {Transfer Learning for Named-Entity Recognition with Neural Networks},
	author       = {Ji Young Lee and Franck Dernoncourt and Peter Szolovits},
	year         = 2017,
	journal      = {CoRR},
	volume       = {abs/1705.06273},
	url          = {http://arxiv.org/abs/1705.06273},
	archiveprefix = {arXiv},
	eprint       = {1705.06273},
	timestamp    = {Mon, 13 Aug 2018 16:48:06 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/LeeDS17a},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{transductive-2007,
	title        = {Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification},
	author       = {Blitzer, John  and Dredze, Mark  and Pereira, Fernando},
	year         = 2007,
	month        = jun,
	booktitle    = {Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics},
	address      = {Prague, Czech Republic},
	pages        = {440--447},
	url          = {https://www.aclweb.org/anthology/P07-1056}
}
@inproceedings{word2vec-2013,
	title        = {Distributed Representations of Words and Phrases and Their Compositionality},
	author       = {Mikolov, Tomas and Sutskever, Ilya and Chen, Kai and Corrado, Greg and Dean, Jeffrey},
	year         = 2013,
	booktitle    = {Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2},
	location     = {Lake Tahoe, Nevada, US},
	address      = {US},
	series       = {Conference on Neural Information Processing Systems},
	pages        = {3111--3119},
	url          = {http://dl.acm.org/citation.cfm?id=2999792.2999959},
	numpages     = 9,
	acmid        = 2999959
}
@article{weak_supervision_mri,
	title        = {Weakly supervised classification of rare aortic valve malformations using unlabeled cardiac MRI sequences},
	author       = {Fries, Jason and Varma, Paroma and Chen, Vincent and Xiao, Ke and Tejeda, Heliodoro and Saha, Priyanka and Dunnmon, Jared and Chubb, Henry and Maskatia, Shiraz and Fiterau, Madalina and Delp, Scott and Ashley, Euan and Re, Christopher and Priest, James},
	year         = 2018,
	journal      = {bioRxiv},
	doi          = {10.1101/339630},
	url          = {https://www.biorxiv.org/content/early/2018/08/22/339630},
	elocation-id = 339630,
	abstract     = {Population-scale biomedical repositories such as the UK Biobank provide unprecedented access to prospectively collected cardiac imaging data, however the majority of these data are unlabeled, creating barriers to their use in supervised machine learning. We developed a weakly supervised deep learning model for Bicuspid Aortic Valve (BAV) classification using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring curated, hand-labeled training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For BAV classification, training models using these imperfect labels substantially outperformed a traditional supervised model trained on hand-labeled MRIs. In a validation experiment using long-term outcome data from the UK Biobank, our classification model identified a subset of individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes the first deep learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using large collections of unlabeled medical images.},
	eprint       = {https://www.biorxiv.org/content/early/2018/08/22/339630.full.pdf}
}
@incollection{physics_weak_supervision_2018,
	title        = {Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems},
	author       = {Sachan, Mrinmaya and Dubey, Kumar Avinava and Mitchell, Tom M and Roth, Dan and Xing, Eric P},
	year         = 2018,
	booktitle    = {NIPS 31},
	pages        = {140--151},
	url          = {http://papers.nips.cc/paper/7299-learning-pipelines-with-limited-data-and-domain-knowledge-a-study-in-parsing-physics-problems.pdf}
}
@article{word_embeddings_stability_2018,
	title        = {Evaluating the Stability of Embedding-based Word Similarities},
	author       = {Antoniak, Maria  and Mimno, David},
	year         = 2018,
	journal      = {Transactions of the Association for Computational Linguistics},
	volume       = 6,
	pages        = {107--119},
	url          = {https://www.aclweb.org/anthology/Q18-1008},
	abstract     = {Word embeddings are increasingly being used as a tool to study word associations in specific corpora. However, it is unclear whether such embeddings reflect enduring properties of language or if they are sensitive to inconsequential variations in the source documents. We find that nearest-neighbor distances are highly sensitive to small changes in the training corpus for a variety of algorithms. For all methods, including specific documents in the training set can result in substantial variations. We show that these effects are more prominent for smaller training corpora. We recommend that users never rely on single embedding models for distance calculations, but rather average over multiple bootstrap samples, especially for small corpora.}
}
@article{emrQA_2018,
	title        = {emrQA: {A} Large Corpus for Question Answering on Electronic Medical Records},
	author       = {Anusri Pampari and Preethi Raghavan and Jennifer Liang and Jian Peng},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1809.00732},
	url          = {http://arxiv.org/abs/1809.00732},
	archiveprefix = {arXiv},
	eprint       = {1809.00732},
	timestamp    = {Fri, 05 Oct 2018 11:34:52 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1809-00732},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{i2b2_dataset,
	title        = {Identifying personal health information using support vector machines},
	author       = {Yikun Guo and Robert Gaizauskas and Ian Roberts and George Demetriou},
	year         = 2006,
	booktitle    = {i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data},
	address      = {Washington, DC, US}
}
@article{patient_understanding_2008,
	title        = {Do patients understand?},
	author       = {Graham, Suzanne and Brookey, John},
	year         = 2008,
	journal      = {The Permanente journal},
	publisher    = {The Permanente Journal},
	volume       = 12,
	number       = 3,
	pages        = {67--69},
	doi          = {10.7812/tpp/07-144},
	issn         = {1552-5767},
	url          = {https://www.ncbi.nlm.nih.gov/pubmed/21331214}
}
@article{multi-task-medical-NER-2018,
	title        = {A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization},
	author       = {Sendong Zhao and Ting Liu and Sicheng Zhao and Fei Wang},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1812.06081},
	url          = {http://arxiv.org/abs/1812.06081},
	archiveprefix = {arXiv},
	eprint       = {1812.06081},
	timestamp    = {Tue, 01 Jan 2019 15:01:25 +0100},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1812-06081},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{longer_transformer_sequences,
	title        = {Generating Wikipedia by Summarizing Long Sequences},
	author       = {Peter Liu and Mohammad Saleh and Etienne Pot and Ben Goodrich and Ryan Sepassi and Lukasz Kaiser and Noam Shazeer},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1801.10198},
	url          = {http://arxiv.org/abs/1801.10198},
	archiveprefix = {arXiv},
	eprint       = {1801.10198},
	timestamp    = {Mon, 13 Aug 2018 16:49:07 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1801-10198},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{text_summarisation_strategies,
	title        = {Neural Abstractive Text Summarization with Sequence-to-Sequence Models},
	author       = {Tian Shi and Yaser Keneshloo and Naren Ramakrishnan and Chandan Reddy},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1812.02303},
	url          = {http://arxiv.org/abs/1812.02303},
	archiveprefix = {arXiv},
	eprint       = {1812.02303},
	timestamp    = {Tue, 01 Jan 2019 15:01:25 +0100},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1812-02303},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{text2table_summarisation,
	title        = {TEXT2TABLE: Medical Text Summarization System Based on Named Entity Recognition and Modality Identification},
	author       = {Aramaki, Eiji and Miura, Yasuhide and Tonoike, Masatsugu and Ohkuma, Tomoko and Mashuichi, Hiroshi and Ohe, Kazuhiko},
	year         = 2009,
	booktitle    = {Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing},
	location     = {Boulder, Colorado},
	address      = {Stroudsburg, PA, US},
	series       = {Biomedical Natural Language Processing},
	pages        = {185--192},
	isbn         = {978-1-932432-30-5},
	url          = {http://dl.acm.org/citation.cfm?id=1572364.1572390},
	numpages     = 8,
	acmid        = 1572390
}
@article{text_summarisation_review,
	title        = {Text summarization in the biomedical domain: a systematic review of recent research},
	author       = {Mishra, Rashmi and Bian, Jiantao and Fiszman, Marcelo and Weir, Charlene and Jonnalagadda, Siddhartha and Mostafa, Javed and Del Fiol, Guilherme},
	year         = 2014,
	month        = {Dec},
	journal      = {Journal of Biomedical Informatics},
	volume       = 52,
	pages        = {457--467},
	doi          = {10.1016/j.jbi.2014.06.009},
	issn         = {1532-0480},
	url          = {https://www.ncbi.nlm.nih.gov/pubmed/25016293}
}
@article{semantic_change,
	title        = {Tracking word semantic change in biomedical literature},
	author       = {Erjia Yan and Yongjun Zhu},
	year         = 2018,
	journal      = {International Journal of Medical Informatics},
	volume       = 109,
	pages        = {76--86},
	doi          = {https://doi.org/10.1016/j.ijmedinf.2017.11.006},
	issn         = {1386-5056},
	url          = {http://www.sciencedirect.com/science/article/pii/S1386505617304185},
	keywords     = {PubMed, Semantic change, Topic modeling, Skip-gram, Word2vec},
	abstract     = {Up to this point, research on written scholarly communication has focused primarily on syntactic, rather than semantic, analyses. Consequently, we have yet to understand semantic change as it applies to disciplinary discourse. The objective of this study is to illustrate word semantic change in biomedical literature. To that end, we identify a set of representative words in biomedical literature based on word frequency and word-topic probability distributions. A word2vec language model is then applied to the identified words in order to measure word- and topic-level semantic changes. We find that for the selected words in PubMed, overall, meanings are becoming more stable in the 2000s than they were in the 1980s and 1990s. At the topic level, the global distance of most topics (19 out of 20 tested) is declining, suggesting that the words used to discuss these topics are stabilizing semantically. Similarly, the local distance of most topics (19 out of 20) is also declining, showing that the meanings of words from these topics are becoming more consistent with those of their semantic neighbors. At the word level, this paper identifies two different trends in word semantics, as measured by the aforementioned distance metrics: on the one hand, words can form clusters with their semantic neighbors, and these words, as a cluster, coevolve semantically; on the other hand, words can drift apart from their semantic neighbors while nonetheless stabilizing in the global context. In relating our work to language laws on semantic change, we find no overwhelming evidence to support either the law of parallel change or the law of conformity.}
}
@article{biomedical_language_problems,
	title        = {Biomedical language processing: what's beyond PubMed?},
	author       = {Hunter, Lawrence and Cohen, Bretonnel},
	year         = 2006,
	month        = {Mar},
	day          = {03},
	journal      = {Molecular Cell},
	volume       = 21,
	number       = 5,
	pages        = {589--594},
	doi          = {10.1016/j.molcel.2006.02.012},
	issn         = {1097-2765},
	url          = {https://www.ncbi.nlm.nih.gov/pubmed/16507357}
}
@article{nmt_embedding,
	title        = {When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?},
	author       = {Ye Qi and Devendra Sachan and Matthieu Felix and Sarguna Padmanabhan and Graham Neubig},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1804.06323},
	url          = {http://arxiv.org/abs/1804.06323},
	archiveprefix = {arXiv},
	eprint       = {1804.06323},
	timestamp    = {Mon, 13 Aug 2018 16:48:29 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1804-06323},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{nmt_embedding_loss,
	title        = {Training Neural Machine Translation using Word Embedding-based Loss},
	author       = {Katsuki Chousa and Katsuhito Sudoh and Satoshi Nakamura},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1807.11219},
	url          = {http://arxiv.org/abs/1807.11219},
	archiveprefix = {arXiv},
	eprint       = {1807.11219},
	timestamp    = {Mon, 13 Aug 2018 16:48:55 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1807-11219},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{n-gram-embedding,
	title        = {Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics},
	author       = {Zhao, Zhe  and Liu, Tao  and Li, Shen  and Li, Bofang  and Du, Xiaoyong},
	year         = 2017,
	month        = sep,
	booktitle    = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
	address      = {Copenhagen, Denmark},
	pages        = {244--253},
	doi          = {10.18653/v1/D17-1023},
	url          = {https://www.aclweb.org/anthology/D17-1023},
	abstract     = {The existing word representation methods mostly limit their information source to word co-occurrence statistics. In this paper, we introduce ngrams into four representation methods: SGNS, GloVe, PPMI matrix, and its SVD factorization. Comprehensive experiments are conducted on word analogy and similarity tasks. The results show that improved word representations are learned from ngram co-occurrence statistics. We also demonstrate that the trained ngram representations are useful in many aspects such as finding antonyms and collocations. Besides, a novel approach of building co-occurrence matrix is proposed to alleviate the hardware burdens brought by ngrams.}
}
@book{word-sense-disambiguation,
	title        = {Word Sense Disambiguation of Medical Terms via Recurrent Convolutional Neural Networks},
	author       = {Sven Festag and Cord Spreckelsen},
	year         = 2017,
	publisher    = {Health Informatics Meets eHealth},
	volume       = 236,
	pages        = {8--15},
	doi          = {10.3233/978-1-61499-759-7-8},
	url          = {http://ebooks.iospress.nl/publication/46454}
}
@article{metamap,
	title        = {An overview of {M}etaMap: Historical perspective and recent advances},
	author       = {Aronson, Alan and Lang, Fran{\c{c}}ois-Michel},
	year         = 2010,
	journal      = {Journal of the American Medical Informatics Association},
	volume       = 17,
	number       = 3,
	pages        = {229--236},
	doi          = {10.1136/jAmerican Medical Informatics Association.2009.002733},
	issn         = {1527-974X},
	url          = {https://www.ncbi.nlm.nih.gov/pubmed/20442139},
	abstract     = {MetaMap is a widely available program providing access to the concepts in the unified medical language system (UMLS) Metathesaurus from biomedical text. This study reports on MetaMap's evolution over more than a decade, concentrating on those features arising out of the research needs of the biomedical informatics community both within and outside of the National Library of Medicine. Such features include the detection of author-defined acronyms/abbreviations, the ability to browse the Metathesaurus for concepts even tenuously related to input text, the detection of negation in situations in which the polarity of predications is important, word sense disambiguation (WSD), and various technical and algorithmic features. Near-term plans for MetaMap development include the incorporation of chemical name recognition and enhanced WSD.}
}
@inproceedings{deep_bayesian_nlp,
	title        = {Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study},
	author       = {Siddhant, Aditya  and Lipton, Zachary},
	year         = 2018,
	month        = oct # {-} # nov,
	booktitle    = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
	address      = {Brussels, Belgium},
	pages        = {2904--2909},
	url          = {https://www.aclweb.org/anthology/D18-1318},
	abstract     = {Several recent papers investigate Active Learning (AL) for mitigating the data dependence of deep learning for natural language processing. However, the applicability of AL to real-world problems remains an open question. While in supervised learning, practitioners can try many different methods, evaluating each against a validation set before selecting a model, AL affords no such luxury. Over the course of one AL run, an agent annotates its dataset exhausting its labeling budget. Thus, given a new task, we have no opportunity to compare models and acquisition functions. This paper provides a large-scale empirical study of deep active learning, addressing multiple tasks and, for each, multiple datasets, multiple models, and a full suite of acquisition functions. We find that across all settings, Bayesian active learning by disagreement, using uncertainty estimates provided either by Dropout or Bayes-by-Backprop significantly improves over i.i.d. baselines and usually outperforms classic uncertainty sampling.}
}
@inbook{conformal_prediction,
	title        = {Inductive Conformal Prediction: Theory and Application to Neural Networks},
	author       = {Papadopoulos, Harris},
	year         = 2008,
	month        = {08},
	doi          = {10.5772/6078},
	isbn         = {978-953-7619-03-9}
}
@article{uncertainty_bayesian_computer_vision,
	title        = {What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?},
	author       = {Alex Kendall and Yarin Gal},
	year         = 2017,
	journal      = {CoRR},
	volume       = {abs/1703.04977},
	url          = {http://arxiv.org/abs/1703.04977},
	archiveprefix = {arXiv},
	eprint       = {1703.04977},
	timestamp    = {Mon, 13 Aug 2018 16:48:58 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/KendallG17},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@incollection{space-time-embeddings,
	title        = {Space-Time Local Embeddings},
	author       = {Sun, Ke and Wang, Jun and Kalousis, Alexandros and Marchand-Maillet, Stephane},
	year         = 2015,
	booktitle    = {NIPS 28},
	pages        = {100--108},
	url          = {http://papers.nips.cc/paper/5971-space-time-local-embeddings.pdf}
}
@article{dynamic-word-embeddings,
	title        = {{Dynamic Word Embeddings}},
	author       = {{Bamler}, Robert and {Mandt}, Stephan},
	year         = 2017,
	month        = {Feb},
	journal      = {CoRR},
	pages        = {arXiv:1702.08359},
	keywords     = {Statistics - Machine Learning, Computer Science - Machine Learning},
	eid          = {arXiv:1702.08359},
	archiveprefix = {arXiv},
	eprint       = {1702.08359},
	primaryclass = {stat.ML},
	adsurl       = {https://ui.adsabs.harvard.edu/abs/2017arXiv170208359B},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{time-embedding-emoji,
	title        = {Exploring Emoji Usage and Prediction Through a Temporal Variation Lens},
	author       = {Francesco Barbieri and Luis Marujo and Pradeep Karuturi and William Brendel and Horacio Saggion},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1805.00731},
	url          = {http://arxiv.org/abs/1805.00731},
	archiveprefix = {arXiv},
	eprint       = {1805.00731},
	timestamp    = {Mon, 13 Aug 2018 16:48:28 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1805-00731},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{doc2vec,
	title        = {Distributed Representations of Sentences and Documents},
	author       = {Le, Quoc and Mikolov, Tomas},
	year         = 2014,
	booktitle    = {Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32},
	address      = {Beijing, China},
	series       = {ICML'14},
	pages        = {II-1188--II-1196},
	url          = {http://dl.acm.org/citation.cfm?id=3044805.3045025},
	acmid        = 3045025
}
@inproceedings{document_embedding,
	title        = {Document embedding with paragraph vectors},
	author       = {Andrew Dai and Christopher Olah and Quoc Le},
	year         = 2015,
	booktitle    = {Conference on Neural Information Processing Systems Deep Learning Workshop},
	address      = {Palais des Congr\`{e}s de Montreal, Canada}
}
@article{empriical-doc2vec,
	title        = {An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation},
	author       = {Jey Lau and Timothy Baldwin},
	year         = 2016,
	journal      = {CoRR},
	volume       = {abs/1607.05368},
	url          = {http://arxiv.org/abs/1607.05368},
	archiveprefix = {arXiv},
	eprint       = {1607.05368},
	timestamp    = {Mon, 13 Aug 2018 16:48:35 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/LauB16},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{physiobank,
	title        = {{{P}hysio{B}ank, {P}hysio{T}oolkit, and {P}hysio{N}et: components of a new research resource for complex physiologic signals}},
	author       = {Goldberger, Ary  and Amaral, Luis  and Glass, Leon  and Hausdorff, Jeffrey and Ivanov, Plamen  and Mark, Roger  and Mietus, Joseph  and Moody, George  and Peng, Chung-Kang  and Stanley, Eugene},
	year         = 2000,
	month        = {Jun},
	journal      = {Circulation},
	volume       = 101,
	number       = 23,
	pages        = {E215--220},
	url          = {https://www.ncbi.nlm.nih.gov/pubmed/10851218}
}
@article{mimic,
	title        = {{MIMIC-III}, A freely accessible critical care database},
	author       = {Johnson, Alistair and Pollard, Tom and Shen, Lu and Lehman, Li-wei and Feng, Mengling and Ghassemi, Mohammad and Moody, Benjamin and Szolovits, Peter and Anthony Celi and Mark, Roger},
	year         = 2016,
	month        = {May},
	day          = 24,
	journal      = {Scientific Data},
	publisher    = {Nature Publishing Group},
	volume       = 3,
	number       = 1,
	pages        = 160035,
	doi          = {10.1038/sdata.2016.35},
	issn         = {2052-4463},
	url          = {https://doi.org/10.1038/sdata.2016.35}
}
@article{biobert,
	title        = {{BioBERT: a pre-trained biomedical language representation model for biomedical text mining}},
	author       = {Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
	year         = 2019,
	month        = {09},
	journal      = {Bioinformatics},
	volume       = 36,
	number       = 4,
	pages        = {1234--1240},
	doi          = {10.1093/bioinformatics/btz682},
	issn         = {1367-4803},
	url          = {https://doi.org/10.1093/bioinformatics/btz682},
	eprint       = {http://oup.prod.sis.lan/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btz682/30132027/btz682.pdf}
}
@inproceedings{rogue-2,
	title        = {Evaluation of Automatic Summaries: Metrics Under Varying Data Conditions},
	author       = {Owczarzak, Karolina and Dang, Hoa},
	year         = 2009,
	booktitle    = {Proceedings of the Workshop on Language Generation and Summarisation},
	location     = {Suntec, Singapore},
	address      = {Stroudsburg, PA, US},
	pages        = {23--30},
	isbn         = {978-1-932432-51-0},
	url          = {http://dl.acm.org/citation.cfm?id=1708155.1708161},
	numpages     = 8,
	acmid        = 1708161
}
@inproceedings{rogue,
	title        = {{ROUGE}: A Package for Automatic Evaluation of Summaries},
	author       = {Lin, Chin-Yew},
	year         = 2004,
	month        = jul,
	booktitle    = {Text Summarization Branches Out: Proceedings of the {Association for Computational Linguistics} Workshop},
	address      = {Barcelona, Spain},
	pages        = {74--81},
	url          = {https://www.aclweb.org/anthology/W04-1013}
}
@inproceedings{bleu,
	title        = {Bleu: A Method for Automatic Evaluation of Machine Translation},
	author       = {Papineni, Kishore  and Roukos, Salim  and Ward, Todd  and Zhu, Wei-Jing},
	year         = 2002,
	month        = jul,
	booktitle    = {Proceedings of 40th Annual Meeting of the Association for Computational Linguistics},
	address      = {Philadelphia, Pennsylvania, US},
	pages        = {311--318},
	doi          = {10.3115/1073083.1073135},
	url          = {https://www.aclweb.org/anthology/P02-1040}
}
% 28269825
@article{recognising-question-entailment-2016,
	title        = {{Recognizing Question Entailment for Medical Question Answering}},
	author       = {Ben Abacha and Demner-Fushman},
	year         = 2016,
	journal      = {American Medical Informatics Association Annual Symposium Proceedings},
	address      = {Chicago, US},
	volume       = 2016,
	pages        = {310--318},
	url          = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333286/}
}
@inbook{mean-reciprocal-rank,
	title        = {Mean Reciprocal Rank},
	author       = {Craswell, Nick},
	year         = 2009,
	booktitle    = {Encyclopedia of Database Systems},
	address      = {Boston, MA},
	pages        = {1703--1703},
	doi          = {10.1007/978-0-387-39940-9\_488},
	isbn         = {978-0-387-39940-9},
	url          = {https://doi.org/10.1007/978-0-387-39940-9\_488}
}
@inbook{mean-average-precision,
	title        = {Mean Average Precision},
	year         = 2009,
	booktitle    = {Encyclopedia of Database Systems},
	address      = {Boston, MA},
	pages        = {1703--1703},
	doi          = {10.1007/978-0-387-39940-9\_3032},
	isbn         = {978-0-387-39940-9},
	url          = {https://doi.org/10.1007/978-0-387-39940-9\_3032},
	editor       = {Liu, Ling and {\"O}zsu, Tamer}
}
@article{specificity-sensitivty,
	title        = {Understanding and using sensitivity, specificity and predictive values},
	author       = {Parikh, Rajul and Mathai, Annie and Parikh, Shefali and Chandra Sekhar and Thomas, Ravi},
	year         = 2008,
	journal      = {Indian Journal of Ophthalmology},
	volume       = 56,
	number       = 1,
	pages        = {45--50},
	issn         = {0301-4738},
	url          = {https://www.ncbi.nlm.nih.gov/pubmed/18158403},
	abstract     = {In this article, we have discussed the basic knowledge to calculate sensitivity, specificity, positive predictive value and negative predictive value. We have discussed the advantage and limitations of these measures and have provided how we should use these measures in our day-to-day clinical practice. We also have illustrated how to calculate sensitivity and specificity while combining two tests and how to use these results for our patients in day-to-day practice.}
}
@article{dr_google_patient_doctor,
	title        = {Dr {G}oogle in the {ED}: searching for online health information by adult emergency department patients},
	author       = {Anthony Cocco and Rachel Zordan and David Taylor and Tracey Weiland and Stuart Dilley and Joyce Kant and Mahesha Dombagolla and Andreas Hendarto and Fiona Lai and Jennie Hutton},
	year         = 2018,
	journal      = {The Medical Journal of Australia},
	volume       = 209,
	pages        = {342--347},
	doi          = {10.5694/mja17.00889},
	url          = {https://www.mja.com.au/journal/2018/209/8/dr-google-ed-searching-online-health-information-adult-emergency-departmen},
	issue        = 8
}
@inproceedings{dependency_trees,
	title        = {Mapping Dependencies Trees: An Application to Question Answering},
	author       = {Vasin Punyakanok and Dan Roth and Wen-tau Yih},
	year         = 2004,
	booktitle    = {Proceedings of the 8th International Symposium on Artificial Intelligence and Mathematics, Fort},
	address      = {Fort Lauderdale, Flordia},
	url          = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.4864}
}
@inproceedings{quasi_jeopardy,
	title        = {What is the {J}eopardy Model? A Quasi-Synchronous Grammar for {QA}},
	author       = {Wang, Mengqiu  and Smith, Noah A.  and Mitamura, Teruko},
	year         = 2007,
	month        = jun,
	booktitle    = {Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning ({EMNLP}-{C}o{NLL})},
	address      = {Prague, Czech Republic},
	pages        = {22--32},
	url          = {https://www.aclweb.org/anthology/D07-1003}
}
@inproceedings{bioasq_2013_factoid_qa,
	title        = {Answering Factoid Questions in the Biomedical Domain},
	author       = {Wei\ss{}enborn, Dirk and Tsatsaronis, George and Schroeder, Michael},
	year         = 2013,
	month        = {09},
	booktitle    = {CEUR Workshop Proceedings},
	address      = {Valencia, Spain},
	volume       = 1094,
	url          = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.403.3868}
}
@inproceedings{NCBI_2014_bioasq,
	title        = {{NCBI} at the 2014 {BioASQ} Challenge Task: Large-scale Biomedical Semantic Indexing and Question Answering},
	author       = {Yuqing Mao and Chih-Hsuan Wei and Zhiyong Lu},
	year         = 2014,
	booktitle    = {Conference and Labs of the Evaluation Forum},
	address      = {Sheffield, UK},
	url          = {https://www.semanticscholar.org/paper/NCBI-at-the-2014-BioASQ-Challenge-Task\%3A-Large-scale-Mao-Wei/ade85f13fe19572641743f0090617983f50d0f2e}
}
@inproceedings{2015_biaosq,
	title        = {Results of the {BioASQ} Tasks of the Question Answering Lab at Conference and Labs of the Evaluation Forum 2015},
	author       = {Georgios Balikas and Aris Kosmopoulos and Anastasia Krithara and Georgios Paliouras and Ioannis Kakadiaris},
	year         = 2015,
	booktitle    = {Conference and Labs of the Evaluation Forum},
	address      = {Toulouse, France}
}
@inproceedings{2016_bioasq,
	title        = {Results of the 4th edition of {BioASQ} Challenge},
	author       = {Krithara, Anastasia and Nentidis, Anastasios and Paliouras, Georgios and Kakadiaris, Ioannis},
	year         = 2016,
	month        = {01},
	address      = {Berlin, Germany},
	pages        = {1--7},
	doi          = {10.18653/v1/W16-3101},
	booktitle = {Proceedings of the Fourth BioASQ workshop}
}
@inproceedings{2018_bioasq,
	title        = {Results of the sixth edition of the {BioASQ} Challenge},
	author       = {Nentidis, Anastasios and Krithara, Anastasia  and Bougiatiotis, Konstantinos  and Paliouras, Georgios  and Kakadiaris, Ioannis},
	year         = 2018,
	booktitle    = {Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering},
	address      = {Brussels, Belgium},
	pages        = {1--10},
	url          = {https://www.aclweb.org/anthology/W18-5301},
	abstract     = {This paper presents the results of the sixth edition}
}
@inproceedings{2017_bioasq,
	title        = {Results of the fifth edition of the BioASQ Challenge},
	author       = {Nentidis, Anastasios  and Bougiatiotis, Konstantinos  and Krithara, Anastasia  and Paliouras, Georgios  and Kakadiaris, Ioannis},
	year         = 2017,
	month        = aug,
	booktitle    = {Biomedical Natural Language Processing},
	address      = {Vancouver, Canada},
	pages        = {48--57},
	doi          = {10.18653/v1/W17-2306},
	url          = {https://www.aclweb.org/anthology/W17-2306},
	abstract     = {The goal of the BioASQ challenge is to engage researchers into creating cuttingedge biomedical information systems. Specifically, it aims at the promotion of systems and methodologies that are able to deal with a plethora of different tasks in the biomedical domain. This is achieved through the organization of challenges. The fifth challenge consisted of three tasks: semantic indexing, question answering and a new task on information extraction. In total, 29 teams with more than 95 systems participated in the challenge. Overall, as in previous years, the best systems were able to outperform the strong baselines. This suggests that state-of-the art systems are continuously improving, pushing the frontier of research.}
}
@article{paccr,
	title        = {A Position-Aware Deep Model for Relevance Matching in Information Retrieval},
	author       = {Kai Hui and Andrew Yates and Klaus Berberich and Gerard de Melo},
	year         = 2017,
	journal      = {CoRR},
	volume       = {abs/1704.03940},
	url          = {http://arxiv.org/abs/1704.03940},
	archiveprefix = {arXiv},
	eprint       = {1704.03940},
	timestamp    = {Mon, 13 Aug 2018 16:47:06 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/HuiYBM17},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{drmm,
	title        = {A Deep Relevance Matching Model for Ad-hoc Retrieval},
	author       = {Jiafeng Guo and Yixing Fan and Qingyao Ai and Bruce Croft},
	year         = 2017,
	journal      = {CoRR},
	booktitle    = {CIKM},
	address      = {Indianapolis, IN},
	volume       = {abs/1711.08611},
	pages        = {55--64},
	url          = {http://arxiv.org/abs/1711.08611},
	archiveprefix = {arXiv},
	eprint       = {1711.08611},
	timestamp    = {Mon, 13 Aug 2018 16:46:36 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1711-08611},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{abcnn,
	title        = {{ABCNN:} Attention-Based Convolutional Neural Network for Modeling Sentence Pairs},
	author       = {Wenpeng Yin and Hinrich Sch{\"{u}}tze and Bing Xiang and Bowen Zhou},
	year         = 2015,
	journal      = {CoRR},
	volume       = {abs/1512.05193},
	url          = {http://arxiv.org/abs/1512.05193},
	archiveprefix = {arXiv},
	eprint       = {1512.05193},
	timestamp    = {Mon, 13 Aug 2018 16:47:36 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/YinSXZ15},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{lstm,
	title        = {Long Short-Term Memory},
	author       = {Hochreiter, Sepp and Schmidhuber, J\"{u}rgen},
	year         = 1997,
	journal      = {Neural Computing},
	volume       = 9,
	number       = 8,
	pages        = {1735--1780},
	doi          = {10.1162/neco.1997.9.8.1735},
	url          = {https://doi.org/10.1162/neco.1997.9.8.1735},
	eprint       = {https://doi.org/10.1162/neco.1997.9.8.1735}
}
@inproceedings{CNN,
	title        = {Object Recognition with Gradient-Based Learning},
	author       = {LeCun, Yann and Haffner, Patrick and Bottou, Leon and Bengio, Yoshua},
	year         = 1999,
	booktitle    = {Shape, Contour and Grouping in Computer Vision},
	address      = {London, UK},
	pages        = 319,
	isbn         = {3-540-66722-9},
	url          = {http://dl.acm.org/citation.cfm?id=646469.691875},
	acmid        = 691875
}
@article{question_answering_defintion,
	title        = {An Overview of Question Answering System},
	author       = {Mervin, R.},
	year         = 2013,
	month        = 10,
	url          = {https://www.researchgate.net/publication/320978810\_An\_Overview\_of\_Question\_Answering\_System}
}
@article{amazon_prize_winner_chatbot,
	title        = {Conversational {AI:} The Science Behind the Alexa Prize},
	author       = {Ashwin Ram and Rohit Prasad and Chandra Khatri and Anu Venkatesh and Raefer Gabriel and Qing Liu and Jeff Nunn and Behnam Hedayatnia and Ming Cheng and Ashish Nagar and Eric King and Kate Bland and Amanda Wartick and Yi Pan and Han Song and Sk Jayadevan and Gene Hwang and Art Pettigrue},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1801.03604},
	url          = {http://arxiv.org/abs/1801.03604},
	archiveprefix = {arXiv},
	eprint       = {1801.03604},
	timestamp    = {Mon, 13 Aug 2018 16:47:29 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1801-03604},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{search_engine_question_answering,
	title        = {Notice of Retraction Question Answering Search engine short review and road-map to future QA Search Engine},
	author       = {Aniket {Kadam} and Shashank {Joshi} and Sachin {Shinde} and Sampat {Medhane}},
	year         = 2015,
	month        = {Jan},
	booktitle    = {International Conference on Electrical, Electronics, Signals, Communication and Optimization},
	address      = {Visakhapatnam,  India},
	pages        = {1--8},
	doi          = {10.1109/EESCO.2015.7253949}
}
@article{virtual_assistant_lab,
	title        = {Introducing a Virtual Assistant to the Lab: A Voice User Interface for the Intuitive Control of Laboratory Instruments},
	author       = {Austerjost, Jonas and Porr, Marc and Riedel, Noah and Geier, Dominik and Becker, Thomas and Scheper, Thomas and Marquard, Daniel and Lindner, Patrick and Beutel, Sascha},
	year         = 2018,
	month        = {07},
	journal      = {SLAS Technology: Translating Life Sciences Innovation},
	volume       = 23,
	pages        = {476--482},
	doi          = {10.1177/2472630318788040}
}
@inproceedings{coreference_resolution_QA,
	title        = {Importance of Pronominal Anaphora Resolution in Question Answering Systems},
	author       = {Vicedo, Jose and Ferrandez, Antonio},
	year         = 2000,
	booktitle    = {Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics},
	address      = {Hong Kong},
	pages        = {555--562},
	doi          = {10.3115/1075218.1075288},
	url          = {https://www.aclweb.org/anthology/P00-1070}
}
@inproceedings{named_entity_qa,
	title        = {Named Entity Recognition for Question Answering},
	author       = {Diego Aliod and Menno Zaanen and Daniel Smith},
	year         = 2006,
	booktitle    = {The Australasian Language Technology Association},
	address      = {Sydney, Australia},
	url          = {https://www.aclweb.org/anthology/U06-1009}
}
@inproceedings{structure_qa,
	title        = {Question answering system on education acts using NLP techniques},
	author       = {Sweta {Lende} and Mukesh {Raghuwanshi}},
	year         = 2016,
	month        = {Feb},
	booktitle    = {World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave)},
	address      = {Coimbatore, India},
	pages        = {1--6},
	doi          = {10.1109/STARTUP.2016.7583963},
	keywords     = {document handling;educational administrative data processing;information retrieval systems;legislation;natural language processing;query processing;question answering (information retrieval);search engines;question answering system;closed domain QA system;education acts sections;NLP techniques;natural language processing techniques;information retrieval;prestructured database;natural language document collection;search engine;document handling;Education;Knowledge discovery;Search engines;Natural languages;Data mining;Conferences;Question Answering;NLP;Information Retrieval;Education Acts}
}
@inproceedings{demand_system_qa,
	title        = {An intelligent question answering system for {ICT}},
	author       = {Sameerchand {Pudaruth} and Kajal {Boodhoo} and Lushika {Goolbudun}},
	year         = 2016,
	month        = {March},
	booktitle    = {2016 International Conference on Electrical, Electronics, and Optimization Techniques},
	address      = {Paralakhemundi, Odisha, India},
	pages        = {2895--2899},
	doi          = {10.1109/ICEEOT.2016.7755228},
	keywords     = {data mining;interactive systems;knowledge based systems;question answering (information retrieval);text analysis;intelligent question answering system;ICT;redundant data;World Wide Web;information and communication technology;IT subject;interactive platform;text mining;documents selection;IT questions;structured knowledge base;natural language processing;Knowledge discovery;Knowledge based systems;Natural language processing;Computers;Search engines;question answering;natural language processing;information retrieval;query augmentation}
}
@article{question_answering_overview_open_domain,
	title        = {A literature review on question answering techniques, paradigms and systems},
	author       = {Marco Soares and Fernando Parreiras},
	year         = 2018,
	journal      = {Journal of King Saud University - Computer and Information Sciences},
	doi          = {https://doi.org/10.1016/j.jksuci.2018.08.005},
	issn         = {1319-1578},
	url          = {http://www.sciencedirect.com/science/article/pii/S131915781830082X},
	keywords     = {Question answering systems, Natural language processing, Information retrieval},
	abstract     = {Background Question Answering (QA) systems enable users to retrieve exact answers for questions posed in natural language. Objective This study aims at identifying QA techniques, tools and systems, as well as the metrics and indicators used to measure these approaches for QA systems and also to determine how the relationship between Question Answering and natural language processing is built. Method The method adopted was a Systematic Literature Review of studies published from 2000 to 2017. Results 130 out of 1842 papers have been identified as describing a QA approach developed and evaluated with different techniques. Conclusion Question Answering researchers have concentrated their efforts in natural language processing, knowledge base and information retrieval paradigms. Most of the researches focused on open domain. Regarding the metrics used to evaluate the approaches, Precision and Recall are the most addressed.}
}
@inproceedings{qa_np_enrich,
	title        = {Domain knowledge enriched framework for restricted domain question answering system},
	author       = {Nidhi {Malik} and Aditi {Sharan} and Payal {Biswas}},
	year         = 2013,
	month        = {Dec},
	booktitle    = {IEEE International Conference on Computational Intelligence and Computing Research},
	address      = {Madurai, Tamilnadu, India},
	pages        = {1--7},
	doi          = {10.1109/ICCIC.2013.6724163},
	keywords     = {natural language processing;question answering (information retrieval);domain knowledge enriched framework;restricted domain question answering system;generic architecture;restricted domain question answering system development;NLP resources;Knowledge discovery;Ontologies;Knowledge based systems;Natural languages;Computers;Search engines;answer processing;ontology;question Answering;restricted domain question answering;question processing}
}
@article{comparative_study_metamap,
	title        = {A comparative study of current Clinical Natural Language Processing systems on handling abbreviations in discharge summaries},
	author       = {Wu, Yonghui and Denny, Joshua and Rosenbloom, Trent and Miller, Randolph and Giuse, Dario and Xu, Hua},
	year         = 2012,
	month        = {Nov},
	day          = {03},
	journal      = {American Medical Informatics Association Annual Symposium Proceedings},
	publisher    = {American Medical Informatics Association},
	volume       = 2012,
	pages        = {997--1003},
	issn         = {1942-597X},
	url          = {https://www.ncbi.nlm.nih.gov/pubmed/23304375},
	abstract     = {Clinical Natural Language Processing (NLP) systems extract clinical information from narrative clinical texts in many settings. Previous research mentions the challenges of handling abbreviations in clinical texts, but provides little insight into how well current NLP systems correctly recognize and interpret abbreviations. In this paper, we compared performance of three existing clinical NLP systems in handling abbreviations: MetaMap, MedLEE, and cTAKES. The evaluation used an expert-annotated gold standard set of clinical documents (derived from from 32 de-identified patient discharge summaries) containing 1,112 abbreviations. The existing NLP systems achieved suboptimal performance in abbreviation identification, with F-scores ranging from 0.165 to 0.601. MedLEE achieved the best F-score of 0.601 for all abbreviations and 0.705 for clinically relevant abbreviations. This study suggested that accurate identification of clinical abbreviations is a challenging task and that more advanced abbreviation recognition modules might improve existing clinical NLP systems.}
}
@article{tfidf_biomedical_IR,
	title        = {Beyond information retrieval--medical question answering},
	author       = {Lee, Minsuk and Cimino, James and Zhu, Hai and Sable, Carl and Shanker, Vijay and Ely, John and Yu, Hong},
	year         = 2006,
	journal      = {American Medical Informatics Association Annual Symposium Proceedings},
	publisher    = {American Medical Informatics Association},
	volume       = 2006,
	pages        = {469--473},
	issn         = {1942-597X},
	url          = {https://www.ncbi.nlm.nih.gov/pubmed/17238385},
	abstract     = {Physicians have many questions when caring for patients, and frequently need to seek answers for their questions. Information retrieval systems (e.g., PubMed) typically return a list of documents in response to a user's query. Frequently the number of returned documents is large and makes physicians' information seeking "practical only 'after hours' and not in the clinical settings". Question answering techniques are based on automatically analyzing thousands of electronic documents to generate short-text answers in response to clinical questions that are posed by physicians. The authors address physicians' information needs and described the design, implementation, and evaluation of the medical question answering system (MedQA). Although our long term goal is to enable MedQA to answer all types of medical questions, currently, we implemented MedQA to integrate information retrieval, extraction, and summarization techniques to automatically generate paragraph-level text for definitional questions (i.e., "What is X?"). MedQA can be accessed at http://www.dbmi.columbia.edu/{\textasciitilde}yuh9001/research/MedQA.html.}
}
@article{deep_relevance_ranking,
	title        = {Deep Relevance Ranking Using Enhanced Document-Query Interactions},
	author       = {Ryan McDonald and Georgios{-}Ioannis Brokos and Ion Androutsopoulos},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1809.01682},
	url          = {http://arxiv.org/abs/1809.01682},
	archiveprefix = {arXiv},
	eprint       = {1809.01682},
	timestamp    = {Fri, 05 Oct 2018 11:34:52 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1809-01682},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{train_good_word_embedding_biomed,
	title        = {How to Train good Word Embeddings for Biomedical {NLP}},
	author       = {Chiu, Billy  and Crichton, Gamal  and Korhonen, Anna  and Pyysalo, Sampo},
	year         = 2016,
	month        = aug,
	booktitle    = {Proceedings of the 15th Workshop on Biomedical Natural Language Processing},
	location     = {Berlin, Germany},
	address      = {Berlin, Germany},
	pages        = {166--174},
	doi          = {10.18653/v1/W16-2922},
	url          = {https://www.aclweb.org/anthology/W16-2922}
}
@article{polysemy_embedding,
	title        = {{SECNLP:} {A} Survey of Embeddings in Clinical Natural Language Processing},
	author       = {KS Kalyan and S. Sangeetha},
	year         = 2019,
	journal      = {CoRR},
	volume       = {abs/1903.01039},
	url          = {http://arxiv.org/abs/1903.01039},
	archiveprefix = {arXiv},
	eprint       = {1903.01039},
	timestamp    = {Sat, 30 Mar 2019 19:27:21 +0100},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1903-01039},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{polysemy_embedding_analysis,
	title        = {Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction},
	author       = {Vine, Lance  and Kholghi, Mahnoosh  and Zuccon, Guido  and Sitbon, Laurianne  and Nguyen, Anthony},
	year         = 2015,
	month        = dec,
	booktitle    = {Proceedings of the Australasian Language Technology Association Workshop 2015},
	address      = {Parramatta, Australia},
	pages        = {21--30},
	url          = {https://www.aclweb.org/anthology/U15-1003}
}
@article{wordpiece_tokenisation,
	title        = {{Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation}},
	author       = {{Wu}, Yonghui and {Schuster}, Mike and {Chen}, Zhifeng and {Le}, Quoc V. and {Norouzi}, Mohammad and {Macherey}, Wolfgang and {Krikun}, Maxim and {Cao}, Yuan and {Gao}, Qin and {Macherey}, Klaus and {Klingner}, Jeff and {Shah}, Apurva and {Johnson}, Melvin and {Liu}, Xiaobing and {Kaiser}, {\L}ukasz and {Gouws}, Stephan and {Kato}, Yoshikiyo and {Kudo}, Taku and {Kazawa}, Hideto and {Stevens}, Keith and {Kurian}, George and {Patil}, Nishant and {Wang}, Wei and {Young}, Cliff and {Smith}, Jason and {Riesa}, Jason and {Rudnick}, Alex and {Vinyals}, Oriol and {Corrado}, Greg and {Hughes}, Macduff and {Dean}, Jeffrey},
	year         = 2016,
	month        = {Sep},
	journal      = {CoRR},
	pages        = {arXiv:1609.08144},
	keywords     = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
	eid          = {arXiv:1609.08144},
	archiveprefix = {arXiv},
	eprint       = {1609.08144},
	primaryclass = {cs.CL},
	adsurl       = {https://ui.adsabs.harvard.edu/abs/2016arXiv160908144W},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{commonsenseQA,
	title        = {CommonsenseQA: {A} Question Answering Challenge Targeting Commonsense Knowledge},
	author       = {Alon Talmor and Jonathan Herzig and Nicholas Lourie and Jonathan Berant},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1811.00937},
	url          = {http://arxiv.org/abs/1811.00937},
	archiveprefix = {arXiv},
	eprint       = {1811.00937},
	timestamp    = {Thu, 22 Nov 2018 17:58:30 +0100},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1811-00937},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{clinical_disambig,
	title        = {{{A} long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation ({C}{A}{R}{D})}},
	author       = {Wu, Yonghui and Denny, Joshua  and Trent, Rosenbloom  and Miller, Randolph  and Giuse, Dario  and Wang, Lulu  and Blanquicett, Carmelo  and Soysal, Ergin  and Xu, Jun  and Xu, Hua },
	year         = 2017,
	month        = {Apr},
	journal      = {Journal of the American Medical Informatics Association},
	volume       = 24,
	number       = {e1},
	pages        = {e79-e86},
	url          = {https://www.ncbi.nlm.nih.gov/pubmed/27539197}
}
@inproceedings{clef_ehealth,
	title        = {Overview of the Conference and Labs of the Evaluation Forum eHealth Evaluation Lab 2018},
	author       = {Suominen, Hanna and Kelly, Liadh and Goeuriot, Lorraine and Neveol, Aurelie and Ramadier, Lionel and Robert, Aude and Kanoulas, Evangelos and Spijker, Rene and Azzopardi, Leif and Li, Dan and Jimmy and Palotti, Jo{\~a}o and Zuccon, Guido},
	year         = 2018,
	booktitle    = {Experimental IR Meets Multilinguality, Multimodality, and Interaction},
	address      = {Avignon, France},
	pages        = {286--301},
	isbn         = {978-3-319-98932-7},
	url          = {https://link.springer.com/chapter/10.1007/978-3-319-98932-7\_26},
	editor       = {Bellot, Patrice and Trabelsi, Chiraz and Mothe, Josiane and Murtagh, Fionn and Nie, Jian Yun and Soulier, Laure and SanJuan, Eric and Cappellato, Linda and Ferro, Nicola},
	abstract     = {In this paper, we provide an overview of the sixth annual edition of the Conference and Labs of the Evaluation Forum eHealth evaluation lab. Conference and Labs of the Evaluation Forum eHealth 2018 continues our evaluation resource building efforts around the easing and support of patients, their next-of-kins, clinical staff, and health scientists in understanding, accessing, and authoring eHealth information in a multilingual setting. This year's lab offered three tasks: Task 1 on multilingual information extraction to extend from last year's task on French and English corpora to French, Hungarian, and Italian; Task 2 on technologically assisted reviews in empirical medicine building on last year's pilot task in English; and Task 3 on Consumer Health Search (CHS) in mono- and multilingual settings that builds on the 2013--17 Information Retrieval tasks. In total 28 teams took part in these tasks (14 in Task 1, 7 in Task 2 and 7 in Task 3). Herein, we describe the resources created for these tasks, outline our evaluation methodology adopted and provide a brief summary of participants of this year's challenges and results obtained. As in previous years, the organizers have made data and tools associated with the lab tasks available for future research and development.}
}
@inproceedings{bioasq_oaqa,
	title        = {Tackling Biomedical Text Summarization: {OAQA} at {B}io{ASQ} 5{B}},
	author       = {Chandu, Khyathi  and Naik, Aakanksha  and Chandrasekar, Aditya  and Yang, Zi  and Gupta, Niloy  and Nyberg, Eric},
	year         = 2017,
	month        = aug,
	booktitle    = {{B}io{NLP} 2017},
	address      = {Vancouver, Canada,},
	pages        = {58--66},
	doi          = {10.18653/v1/W17-2307},
	url          = {https://www.aclweb.org/anthology/W17-2307},
	abstract     = {In this paper, we describe our participation in phase B of task 5b of the fifth edition of the annual BioASQ challenge, which includes answering factoid, list, yes-no and summary questions from biomedical data. We describe our techniques with an emphasis on ideal answer generation, where the goal is to produce a relevant, precise, non-redundant, query-oriented summary from multiple relevant documents. We make use of extractive summarization techniques to address this task and experiment with different biomedical ontologies and various algorithms including agglomerative clustering, Maximum Marginal Relevance (MMR) and sentence compression. We propose a novel word embedding based tf-idf similarity metric and a soft positional constraint which improve our system performance. We evaluate our techniques on test batch 4 from the fourth edition of the challenge. Our best system achieves a ROUGE-2 score of 0.6534 and ROUGE-SU4 score of 0.6536.}
}
@inproceedings{bioasq_fudan,
	title        = {The Fudan Participation in the 2015 BioASQ Challenge: Large-scale Biomedical Semantic Indexing and Question Answering},
	author       = {Shengwen Peng and Ronghui You and Zhikai Xie and Beichen Wang and Yanchun Zhang and Shanfeng Zhu},
	year         = 2015,
	journal      = {CEUR Workshop Proceedings},
	booktitle    = {Conference and Labs of the Evaluation Forum 2015: Conference and Labs of the Evaluation Forum Experimental IR meets Multilinguality, Multimodality and Interaction},
	address      = {Toulouse, France},
	volume       = 1391,
	url          = {http://vuir.vu.edu.au/30662/},
	keywords     = {MeSH Indexing; Learning to Rank; Multi-Label Classification; Biomedical Question Answering; Information Retrieval; Information Extraction}
}
@inproceedings{context_aware_developer_assistant,
	title        = {Context-aware Conversational Developer Assistants},
	author       = {Bradley, Nick and Fritz, Thomas and Holmes, Reid},
	year         = 2018,
	booktitle    = {Proceedings of the 40th International Conference on Software Engineering},
	location     = {Gothenburg, Sweden},
	address      = {New York, NY, US},
	pages        = {993--1003},
	doi          = {10.1145/3180155.3180238},
	isbn         = {978-1-4503-5638-1},
	url          = {http://doi.acm.org/10.1145/3180155.3180238},
	numpages     = 11,
	acmid        = 3180238,
	keywords     = {conversational development assistants, natural user interfaces}
}
@inproceedings{human_bot_social,
	title        = {Among the Machines: Human-Bot Interaction on Social Q\&\#38;A Websites},
	author       = {Murgia, Alessandro and Janssens, Daan and Demeyer, Serge and Vasilescu, Bogdan},
	year         = 2016,
	booktitle    = {Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems},
	location     = {San Jose, California, US},
	address      = {New York, NY, US},
	series       = {CHI EA '16},
	pages        = {1272--1279},
	doi          = {10.1145/2851581.2892311},
	isbn         = {978-1-4503-4082-3},
	url          = {http://doi.acm.org/10.1145/2851581.2892311},
	numpages     = 8,
	acmid        = 2892311,
	keywords     = {social bot, stack overflow, turing test}
}
@inproceedings{alimet_chat,
	title        = {{A}li{M}e Chat: A Sequence to Sequence and Rerank based Chatbot Engine},
	author       = {Qiu, Minghui  and Li, Feng-Lin  and Wang, Siyu  and Gao, Xing  and Chen, Yan  and Zhao, Weipeng  and Chen, Haiqing  and Huang, Jun  and Chu, Wei},
	year         = 2017,
	month        = jul,
	booktitle    = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
	address      = {Vancouver, Canada},
	pages        = {498--503},
	doi          = {10.18653/v1/P17-2079},
	url          = {https://www.aclweb.org/anthology/P17-2079},
	abstract     = {We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models. AliMe Chat uses an attentive Seq2Seq based rerank model to optimize the joint results. Extensive experiments show our engine outperforms both IR and generation based models. We launch AliMe Chat for a real-world industrial application and observe better results than another public chatbot.}
}
@inproceedings{ehealth_chatbot,
	title        = {Chatbots Meet eHealth: Automatizing Healthcare},
	author       = {Flora Amato and Stefano Marrone and Vincenzo Moscato and Gabriele Piantadosi and Antonio Picariello and Carlo Sansone},
	year         = 2017,
	booktitle    = {Proceedings of the Workshop on Artificial Intelligence with Application in Health co-located with the 16th International Conference of the Italian Association for Artificial Intelligence},
	address      = {Bari, Italy},
	url          = {http://ceur-ws.org/Vol-1982/paper6.pdf}
}
@article{wordnet_medical_qa,
	title        = {{{A} knowledge based method for the medical question answering problem}},
	author       = {Terol, Rafael and Martinez-Barco, Patricio and Palomar, Manuel},
	year         = 2007,
	month        = {Oct},
	journal      = {Computers in Biology and Medicine},
	volume       = 37,
	number       = 10,
	pages        = {1511--1521},
	url          = {https://www.ncbi.nlm.nih.gov/pubmed/17374369}
}
@article{diego_qa_overview,
	title        = {Question Answering in Restricted Domains: An Overview},
	author       = {Molla, Diego and Gonzalez, Jose},
	year         = 2007,
	month        = {03},
	journal      = {Computational Linguistics},
	volume       = 33,
	pages        = {41--61},
	doi          = {10.1162/coli.2007.33.1.41}
}
@inproceedings{docchat,
	title        = {{D}oc{C}hat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents},
	author       = {Yan, Zhao  and Duan, Nan  and Bao, Junwei  and Chen, Peng  and Zhou, Ming  and Li, Zhoujun  and Zhou, Jianshe},
	year         = 2016,
	month        = aug,
	booktitle    = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
	publisher    = {Association for Computational Linguistics},
	address      = {Berlin, Germany},
	pages        = {516--525},
	doi          = {10.18653/v1/P16-1049},
	url          = {https://www.aclweb.org/anthology/P16-1049}
}
@inproceedings{virtual_assistant_vulnerable,
	title        = {Toward a Virtual Assistant for Vulnerable Users: Designing Careful Interaction},
	author       = {Yaghoubzadeh, Ramin  and Kopp, Stefan},
	year         = 2012,
	month        = jul,
	booktitle    = {Proceedings of the 1st Workshop on Speech and Multimodal Interaction in Assistive Environments},
	publisher    = {Association for Computational Linguistics},
	address      = {Jeju, Republic of Korea},
	pages        = {13--17},
	url          = {https://www.aclweb.org/anthology/W12-3503}
}
@article{nli_clinical,
	title        = {Lessons from Natural Language Inference in the Clinical Domain},
	author       = {Alexey Romanov and Chaitanya Shivade},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1808.06752},
	url          = {http://arxiv.org/abs/1808.06752},
	archiveprefix = {arXiv},
	eprint       = {1808.06752},
	timestamp    = {Sun, 02 Sep 2018 15:01:57 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1808-06752},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{MEDIAQA_2019_OVERVIEW,
	title        = {Overview of the {MEDIQA} 2019 Shared Task on Textual Inference, Question Entailment and Question Answering},
	author       = {Asma {Ben Abacha} and Chaitanya Shivade and Dina Demner-Fushman},
	year         = 2019,
	booktitle    = {Proceedings of the 18th BioNLP Workshop and Shared Task},
	address      = {Florence, Italy},
	pages        = {370--379},
	doi          = {10.18653/v1/W19-5039}
}
@article{transformer-xl,
	title        = {Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context},
	author       = {Zihang Dai and Zhilin Yang and Yiming Yang and Jaime Carbonell and Quoc Le and Ruslan Salakhutdinov},
	year         = 2019,
	journal      = {CoRR},
	volume       = {abs/1901.02860},
	url          = {http://arxiv.org/abs/1901.02860},
	archiveprefix = {arXiv},
	eprint       = {1901.02860},
	timestamp    = {Fri, 01 Feb 2019 13:39:59 +0100},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1901-02860},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@incollection{LambdaRank,
	title        = {Learning to Rank with Nonsmooth Cost Functions},
	author       = {Christopher Burges and Robert Ragno and Quoc Le},
	year         = 2007,
	booktitle    = {NIPS 19},
	pages        = {193--200},
	url          = {http://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf}
}
@inproceedings{RankNet,
	title        = {Learning to Rank Using Gradient Descent},
	author       = {Burges, Chris and Shaked, Tal and Renshaw, Erin and Lazier, Ari and Deeds, Matt and Hamilton, Nicole and Hullender, Greg},
	year         = 2005,
	booktitle    = {Proceedings of the 22Nd International Conference on Machine Learning},
	location     = {Bonn, Germany},
	publisher    = {ACM},
	address      = {New York, NY},
	series       = {ICML '05},
	pages        = {89--96},
	doi          = {10.1145/1102351.1102363},
	isbn         = {1-59593-180-5},
	url          = {http://doi.acm.org/10.1145/1102351.1102363},
	numpages     = 8,
	acmid        = 1102363
}
@inproceedings{adam_optimizer,
	title        = {Adam: {A} Method for Stochastic Optimization},
	author       = {Diederik Kingma and Jimmy Ba},
	year         = 2015,
	booktitle    = {3rd International Conference on Learning Representations},
	address      = {San Diego, CA},
	url          = {http://arxiv.org/abs/1412.6980},
	timestamp    = {Fri, 29 Mar 2019 00:00:00 +0100},
	biburl       = {https://dblp.org/rec/bib/journals/corr/KingmaB14},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{ensemble_CNN_medical_image,
	title        = {An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification},
	author       = {Ashnil Kumar and Jinman Kim and David Lyndon and Michael Fulham and Dagan Feng},
	year         = 2017,
	month        = {Jan},
	journal      = {IEEE Journal of Biomedical and Health Informatics},
	volume       = 21,
	number       = 1,
	pages        = {31--40},
	doi          = {10.1109/JBHI.2016.2635663},
	issn         = {2168-2194}
}
@article{hybrid_ensemble,
	title        = {Hybrid ensemble approach for classification},
	author       = {Verma Brijesh and Hassan Syed Zahid},
	year         = 2011,
	month        = {Apr},
	day          = {01},
	journal      = {Applied Intelligence},
	volume       = 34,
	number       = 2,
	pages        = {258--278},
	doi          = {10.1007/s10489-009-0194-7},
	issn         = {1573-7497},
	url          = {https://doi.org/10.1007/s10489-009-0194-7}
}
@article{federated_search,
	title        = {Merging algorithms for enterprise search},
	author       = {Li, Vincent and Thomas, Paul and Hawking, David},
	year         = 2013,
	month        = 12,
	journal      = {ACM International Conference Proceeding Series},
	pages        = {42--49},
	doi          = {10.1145/2537734.2537750}
}
@inproceedings{BM25,
	title        = {Okapi at {TREC}-3},
	author       = {Stephen Robertson and Steve Walker and Susan Jones and Micheline Hancock-Beaulieu and Mike Gatford},
	year         = 1994,
	booktitle    = {TREC},
	address      = {Gaithersburg, MD},
	url          = {https://www.microsoft.com/en-us/research/publication/okapi-at-trec-3/}
}
@inproceedings{snli,
	title        = {A large annotated corpus for learning natural language inference},
	author       = {Bowman, Samuel and Angeli, Gabor and Potts, Christopher and Manning, Christopher},
	year         = 2015,
	booktitle    = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
	publisher    = {Association for Computational Linguistics},
	address      = {Lisbon, Portugal},
	url          = {http://nlp.stanford.edu/pubs/snli\_paper.pdf}
}
@inproceedings{glue_datasets,
	title        = {{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
	author       = {Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
	year         = 2019,
	booktitle    = {In the Proceedings of International Conference on Learning Representations},
	address      = {Brussels, Belgium},
	pages        = {353--355},
	url          = {https://arxiv.org/pdf/1804.07461}
}
@inproceedings{panlp,
	title        = {{PANLP} at {MEDIQA} 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation},
	author       = {Zhu, Wei  and Zhou, Xiaofeng  and Wang, Keqiang  and Luo, Xun  and Li, Xiepeng  and Ni, Yuan  and Xie, Guotong},
	year         = 2019,
	booktitle    = {Proceedings of the 18th BioNLP Workshop and Shared Task},
	address      = {Florence, Italy},
	pages        = {380--388},
	doi          = {10.18653/v1/W19-5040}
}
@article{wordpiece_clinical_worse,
	title        = {Enhancing Clinical Concept Extraction with Contextual Embedding},
	author       = {Yuqi Si and Jingqi Wang and Hua Xu and Kirk Roberts},
	year         = 2019,
	journal      = {CoRR},
	volume       = {abs/1902.08691},
	url          = {http://arxiv.org/abs/1902.08691},
	archiveprefix = {arXiv},
	eprint       = {1902.08691},
	timestamp    = {Tue, 21 May 2019 18:03:39 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1902-08691},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{roberta,
	title        = {{RoBERTa}: {A} Robustly Optimized {BERT} Pretraining Approach},
	author       = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov},
	year         = 2019,
	journal      = {CoRR},
	volume       = {abs/1907.11692},
	url          = {http://arxiv.org/abs/1907.11692},
	archiveprefix = {arXiv},
	eprint       = {1907.11692},
	timestamp    = {Thu, 01 Aug 2019 08:59:33 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1907-11692},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{xlnet,
	title        = {{XLNet: Generalized Autoregressive Pretraining for Language Understanding}},
	author       = {{Yang}, Zhilin and {Dai}, Zihang and {Yang}, Yiming and {Carbonell}, Jaime and {Salakhutdinov}, Ruslan and {Le}, Quoc V.},
	year         = 2019,
	month        = {Jun},
	journal      = {CoRR},
	pages        = {arXiv:1906.08237},
	keywords     = {Computer Science - Computation and Language, Computer Science - Machine Learning},
	eid          = {arXiv:1906.08237},
	archiveprefix = {arXiv},
	eprint       = {1906.08237},
	primaryclass = {cs.CL},
	adsurl       = {https://ui.adsabs.harvard.edu/abs/2019arXiv190608237Y},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{scibert,
	title        = {{S}ci{BERT}: A Pretrained Language Model for Scientific Text},
	author       = {Beltagy, Iz  and Lo, Kyle  and Cohan, Arman},
	year         = 2019,
	month        = nov,
	booktitle    = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing},
	address      = {Hong Kong, China},
	pages        = {3606--3611},
	doi          = {10.18653/v1/D19-1371},
	url          = {https://www.aclweb.org/anthology/D19-1371},
	abstract     = {Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et. al., 2018) to address the lack of high-quality, large-scale labeled scientific data. SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks. We evaluate on a suite of tasks including sequence tagging, sentence classification and dependency parsing, with datasets from a variety of scientific domains. We demonstrate statistically significant improvements over BERT and achieve new state-of-the-art results on several of these tasks. The code and pretrained models are available at https://github.com/allenai/scibert/.}
}
@inproceedings{BLUE_dataset,
	title        = {Transfer Learning in Biomedical Natural Language Processing: An Evaluation of {BERT} and {ELMo} on Ten Benchmarking Datasets},
	author       = {Yifan Peng and Shankai Yan and Zhiyong Lu},
	year         = 2019,
	booktitle    = {Proceedings of the Workshop on Biomedical Natural Language Processing},
	address      = {Florence, Italy},
	pages        = {58--65},
	url          = {https://arxiv.org/pdf/1906.05474}
}
@inproceedings{ANUCSIRO_mediqa,
	title        = {{ANU-CSIRO at MEDIQA} 2019: Question Answering Using Deep Contextual Knowledge},
	author       = {Nguyen, Vincent and Karimi, Sarvnaz and Xing, Zhenchang},
	year         = 2019,
	booktitle    = {Proceedings of the 18th BioNLP Workshop and Shared Task},
	address      = {Florence, Italy},
	pages        = {478--487},
	doi          = {10.18653/v1/W19-5051}
}
@inproceedings{scispacy,
	title        = {{S}cispa{C}y: Fast and Robust Models for Biomedical Natural Language Processing},
	author       = {Neumann, Mark  and King, Daniel  and Beltagy, Iz  and Ammar, Waleed},
	year         = 2019,
	booktitle    = {Proceedings of the 18th BioNLP Workshop and Shared Task},
	address      = {Florence, Italy},
	pages        = {319--327},
	doi          = {10.18653/v1/W19-5034},
	eprint       = {arXiv:1902.07669},
	abstract     = {Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scispaCy, a new Python library and models for practical biomedical/scientific text processing, which heavily leverages the spaCy library. We detail the performance of two packages of models released in scispaCy and demonstrate their robustness on several tasks and datasets. Models and code are available at https://allenai.github.io/scispacy/.}
}
@inproceedings{albert,
	title        = {{ALBERT: A Lite BERT for Self-supervised Learning of Language Representations}},
	author       = {{Lan}, Zhenzhong and {Chen}, Mingda and {Goodman}, Sebastian and {Gimpel}, Kevin and {Sharma}, Piyush and {Soricut}, Radu},
	year         = 2020,
	month        = {apr},
	booktitle    = {Proceedings of the 8th International Conference on Learning Representations},
	url          = {https://arxiv.org/pdf/1909.11942}
}
@inproceedings{rare_word_problem,
	title        = {Addressing the Rare Word Problem in Neural Machine Translation},
	author       = {Luong, Thang  and Sutskever, Ilya  and Le, Quoc  and Vinyals, Oriol  and Zaremba, Wojciech},
	year         = 2015,
	month        = jul,
	booktitle    = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
	publisher    = {Association for Computational Linguistics},
	address      = {Beijing, China},
	pages        = {11--19},
	doi          = {10.3115/v1/P15-1002},
	url          = {https://www.aclweb.org/anthology/P15-1002}
}
@inproceedings{character-embedding-fallback,
	title        = {Character and Subword-Based Word Representation for Neural Language Modeling Prediction},
	author       = {Labeau, Matthieu  and Allauzen, Alexandre},
	year         = 2017,
	month        = sep,
	booktitle    = {Proceedings of the First Workshop on Subword and Character Level Models in {NLP}},
	publisher    = {Association for Computational Linguistics},
	address      = {Copenhagen, Denmark},
	pages        = {1--13},
	doi          = {10.18653/v1/W17-4101},
	url          = {https://www.aclweb.org/anthology/W17-4101},
	abstract     = {Most of neural language models use different kinds of embeddings for word prediction. While word embeddings can be associated to each word in the vocabulary or derived from characters as well as factored morphological decomposition, these word representations are mainly used to parametrize the input, i.e. the context of prediction. This work investigates the effect of using subword units (character and factored morphological decomposition) to build output representations for neural language modeling. We present a case study on Czech, a morphologically-rich language, experimenting with different input and output representations. When working with the full training vocabulary, despite unstable training, our experiments show that augmenting the output word representations with character-based embeddings can significantly improve the performance of the model. Moreover, reducing the size of the output look-up table, to let the character-based embeddings represent rare words, brings further improvement.}
}
@book{essential_medical_terminology,
	title        = {Essential Medical Terminology},
	author       = {Stanfield, P. and Hui, Y.H. and Cross, N.},
	year         = 2008,
	publisher    = {Jones and Bartlett},
	isbn         = 9780763749132,
	url          = {https://books.google.com.au/books?id=VG-z7uyWiHIC},
	note         = {Chapter 2},
	lccn         = 2007013273
}
@inproceedings{clinical_bert,
	title        = {Publicly Available Clinical {BERT} Embeddings},
	author       = {Alsentzer, Emily  and Murphy, John  and Boag, William  and Weng, Wei-Hung  and Jindi, Di  and Naumann, Tristan  and McDermott, Matthew},
	year         = 2019,
	month        = jun,
	booktitle    = {Proceedings of the 2nd Clinical Natural Language Processing Workshop},
	address      = {Minneapolis, MN},
	pages        = {72--78},
	doi          = {10.18653/v1/W19-1909},
	url          = {https://www.aclweb.org/anthology/W19-1909},
	abstract     = {Contextual word embedding models such as ELMo and BERT have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been minimally explored on specialty corpora, such as clinical text; moreover, in the clinical domain, no publicly-available pre-trained BERT models yet exist. In this work, we address this need by exploring and releasing BERT models for clinical text: one for generic clinical text and another for discharge summaries specifically. We demonstrate that using a domain-specific model yields performance improvements on 3/5 clinical NLP tasks, establishing a new state-of-the-art on the MedNLI dataset. We find that these domain-specific models are not as performant on 2 clinical de-identification tasks, and argue that this is a natural consequence of the differences between de-identified source text and synthetically non de-identified task text.}
}
@inproceedings{sentencepiece,
	title        = {{S}entence{P}iece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing},
	author       = {Kudo, Taku  and Richardson, John},
	year         = 2018,
	month        = nov,
	booktitle    = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
	publisher    = {Association for Computational Linguistics},
	address      = {Brussels, Belgium},
	pages        = {66--71},
	doi          = {10.18653/v1/D18-2012},
	url          = {https://www.aclweb.org/anthology/D18-2012}
}
@inproceedings{1_by_1_convolution,
	title        = {{Network In Network}},
	author       = {{Lin}, Min and {Chen}, Qiang and {Yan}, Shuicheng},
	year         = 2014,
	booktitle    = {Proceedings of 2nd International Conference on Learning Representations},
	address      = {Banff National Park, Canada},
	url          = {https://arxiv.org/pdf/1312.4400}
}
@inproceedings{relu_gradients,
	title        = {Deep Sparse Rectifier Neural Networks},
	author       = {Xavier Glorot and Antoine Bordes and Yoshua Bengio},
	year         = 2011,
	month        = {11--13 Apr},
	booktitle    = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
	publisher    = {PMLR},
	address      = {Fort Lauderdale, FL},
	series       = {Proceedings of Machine Learning Research},
	volume       = 15,
	pages        = {315--323},
	url          = {http://proceedings.mlr.press/v15/glorot11a.html},
	editor       = {Geoffrey Gordon and David Dunson and Miroslav Dudik},
	pdf          = {http://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf},
	abstract     = {While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent neurons, the latter work better for training multi-layer neural networks. This paper shows that rectifying neurons are an even better model of biological neurons and yield equal or better performance than hyperbolic tangent networks in spite of the hard non-linearity and non-differentiability at zero, creating sparse representations with true zeros which seem remarkably suitable for naturally sparse data. Even though they can take advantage of semi-supervised setups with extra-unlabeled data, deep rectifier networks can reach their best performance without requiring any unsupervised pre-training on purely supervised tasks with large labeled datasets. Hence, these results can be seen as a new milestone in the attempts at understanding the difficulty in training deep but purely supervised neural networks, and closing the performance gap between neural networks learnt with and without unsupervised pre-training. [pdf]}
}
@article{sixteen_heads_better_than_one,
	title        = {Are Sixteen Heads Really Better than One?},
	author       = {Paul Michel and Omer Levy and Graham Neubig},
	year         = 2019,
	journal      = {CoRR},
	volume       = {abs/1905.10650},
	url          = {http://arxiv.org/abs/1905.10650},
	archiveprefix = {arXiv},
	eprint       = {1905.10650},
	timestamp    = {Mon, 03 Jun 2019 13:42:33 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1905-10650},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{hugging_face_transformers,
	title        = {{HuggingFace's Transformers: State-of-the-art Natural Language Processing}},
	author       = {{Wolf}, Thomas and {Debut}, Lysandre and {Sanh}, Victor and {Chaumond}, Julien and {Delangue}, Clement and {Moi}, Anthony and {Cistac}, Pierric and {Rault}, Tim and {Louf}, Remi and {Funtowicz}, Morgan and {Brew}, Jamie},
	year         = 2019,
	month        = {Oct},
	journal      = {CoRR},
	pages        = {arXiv:1910.03771},
	keywords     = {Computer Science - Computation and Language},
	eid          = {arXiv:1910.03771},
	archiveprefix = {arXiv},
	eprint       = {1910.03771},
	primaryclass = {cs.CL},
	adsurl       = {https://ui.adsabs.harvard.edu/abs/2019arXiv191003771W},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{bert_structure_language,
	title        = {What Does {BERT} Learn about the Structure of Language?},
	author       = {Jawahar, Ganesh  and Sagot, Beno{\^\i}t  and Seddah, Djame},
	year         = 2019,
	month        = jul,
	booktitle    = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
	address      = {Florence, Italy},
	pages        = {3651--3657},
	doi          = {10.18653/v1/P19-1356},
	url          = {https://www.aclweb.org/anthology/P19-1356},
	abstract     = {BERT is a recent language representation model that has surprisingly performed well in diverse language understanding benchmarks. This result indicates the possibility that BERT networks capture structural information about language. In this work, we provide novel support for this claim by performing a series of experiments to unpack the elements of English language structure learned by BERT. Our findings are fourfold. BERT{'}s phrasal representation captures the phrase-level information in the lower layers. The intermediate layers of BERT compose a rich hierarchy of linguistic information, starting with surface features at the bottom, syntactic features in the middle followed by semantic features at the top. BERT requires deeper layers while tracking subject-verb agreement to handle long-term dependency problem. Finally, the compositional scheme underlying BERT mimics classical, tree-like structures.}
}
@inproceedings{bert_classical_pipeline,
	title        = {{BERT} Rediscovers the Classical {NLP} Pipeline},
	author       = {Tenney, Ian  and Das, Dipanjan  and Pavlick, Ellie},
	year         = 2019,
	month        = jul,
	booktitle    = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
	address      = {Florence, Italy},
	pages        = {4593--4601},
	doi          = {10.18653/v1/P19-1452},
	url          = {https://www.aclweb.org/anthology/P19-1452},
	abstract     = {Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captured within the network. We find that the model represents the steps of the traditional NLP pipeline in an interpretable and localizable way, and that the regions responsible for each step appear in the expected sequence: POS tagging, parsing, NER, semantic roles, then coreference. Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically, revising lower-level decisions on the basis of disambiguating information from higher-level representations.}
}
@article{vanishing_gradient_problem,
	title        = {The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions},
	author       = {Hochreiter, Sepp},
	year         = 1998,
	month        = apr,
	journal      = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},
	address      = {River Edge, NJ},
	volume       = 6,
	number       = 2,
	pages        = {107--116},
	doi          = {10.1142/S0218488598000094},
	issn         = {0218-4885},
	url          = {http://dx.doi.org/10.1142/S0218488598000094},
	issue_date   = {April 1998},
	numpages     = 10,
	acmid        = 355233,
	keywords     = {long short-term memory, long-term dependencies, recurrent neural nets, vanishing gradient}
}
@inproceedings{fast_dropout,
	title        = {Fast dropout training},
	author       = {Sida Wang and Christopher Manning},
	year         = 2013,
	month        = {17--19 Jun},
	booktitle    = {Proceedings of the 30th International Conference on Machine Learning},
	address      = {Atlanta, GA},
	series       = {Proceedings of Machine Learning Research},
	volume       = 28,
	pages        = {118--126}
}
@article{spanbert,
	title        = {SpanBERT: Improving Pre-training by Representing and Predicting Spans},
	author       = {Mandar Joshi and Danqi Chen and Yinhan Liu and Daniel S. Weld and Luke Zettlemoyer and Omer Levy},
	year         = 2019,
	journal      = {CoRR},
	volume       = {abs/1907.10529},
	url          = {http://arxiv.org/abs/1907.10529},
	archiveprefix = {arXiv},
	eprint       = {1907.10529},
	timestamp    = {Thu, 01 Aug 2019 08:59:33 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1907-10529},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{VDCNN,
	title        = {Very Deep Convolutional Networks for Text Classification},
	author       = {Conneau, Alexis  and Schwenk, Holger  and Barrault, Loic  and Lecun, Yann},
	year         = 2017,
	month        = apr,
	booktitle    = {Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics},
	address      = {Valencia, Spain},
	pages        = {1107--1116},
	url          = {https://www.aclweb.org/anthology/E17-1104},
	abstract     = {The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the deep convolutional networks which have pushed the state-of-the-art in computer vision. We present a new architecture (VDCNN) for text processing which operates directly at the character level and uses only small convolutions and pooling operations. We are able to show that the performance of this model increases with the depth: using up to 29 convolutional layers, we report improvements over the state-of-the-art on several public text classification tasks. To the best of our knowledge, this is the first time that very deep convolutional nets have been applied to text processing.}
}
@inproceedings{sentence_class_cnn,
	title        = {Convolutional Neural Networks for Sentence Classification},
	author       = {Kim, Yoon},
	year         = 2014,
	month        = oct,
	booktitle    = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing},
	address      = {Doha, Qatar},
	pages        = {1746--1751},
	doi          = {10.3115/v1/D14-1181},
	url          = {https://www.aclweb.org/anthology/D14-1181}
}
@article{cnn_architecture,
	title        = {Natural Language Processing (Almost) from Scratch},
	author       = {Collobert, Ronan and Weston, Jason and Bottou, Leon and Karlen, Michael and Kavukcuoglu, Koray and Kuksa, Pavel},
	year         = 2011,
	month        = nov,
	journal      = {Journal of Machine Learning Research},
	volume       = 12,
	pages        = {2493--2537},
	issn         = {1532-4435},
	url          = {http://dl.acm.org/citation.cfm?id=1953048.2078186},
	issue_date   = {2/1/2011},
	numpages     = 45,
	acmid        = 2078186
}
@inproceedings{convolutional_sent_modelling,
	title        = {A Convolutional Neural Network for Modelling Sentences},
	author       = {Kalchbrenner, Nal  and Grefenstette, Edward  and Blunsom, Phil},
	year         = 2014,
	month        = jun,
	booktitle    = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics},
	publisher    = {Association for Computational Linguistics},
	address      = {Baltimore, MD},
	pages        = {655--665},
	doi          = {10.3115/v1/P14-1062},
	url          = {https://www.aclweb.org/anthology/P14-1062}
}
@incollection{seq2seq,
	title        = {Sequence to Sequence Learning with Neural Networks},
	author       = {Sutskever, Ilya and Vinyals, Oriol and Le, Quoc V},
	year         = 2014,
	booktitle    = {NIPS 27},
	publisher    = {Curran Associates, Inc.},
	pages        = {3104--3112},
	url          = {http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf},
	editor       = {Z. Ghahramani and M. Welling and C. Cortes and N. D. Lawrence and K. Q. Weinberger}
}
@inproceedings{orig_batch_normalisation_paper,
	title        = {{Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift}},
	author       = {{Ioffe}, Sergey and {Szegedy}, Christian},
	year         = 2015,
	month        = {Feb},
	booktitle    = {Proceedings  of  the 32nd International  Conference  on  Machine Learning},
	address      = {Lille, France},
	volume       = 37,
	url          = {http://proceedings.mlr.press/v37/ioffe15.pdf}
}
@inproceedings{orig_adamw_paper,
	title        = {{Decoupled Weight Decay Regularization}},
	author       = {{Loshchilov}, Ilya and {Hutter}, Frank},
	year         = 2017,
	booktitle    = {ICLR},
	address      = {Toulon, France},
	url          = {https://ui.adsabs.harvard.edu/abs/2017arXiv171105101L}
}
@article{one_cycle_policy,
	title        = {{A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay}},
	author       = {{Smith}, Leslie N.},
	year         = 2018,
	month        = {Mar},
	journal      = {CoRR},
	pages        = {arXiv:1803.09820},
	url          = {https://ui.adsabs.harvard.edu/abs/2018arXiv180309820S},
	keywords     = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning},
	archiveprefix = {arXiv},
	eprint       = {1803.09820},
	primaryclass = {cs.LG},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{lamb,
	title        = {{Large Batch Optimization for Deep Learning: Training BERT in 76 minutes}},
	author       = {{You}, Yang and {Li}, Jing and {Reddi}, Sashank and {Hseu}, Jonathan and {Kumar}, Sanjiv and {Bhojanapalli}, Srinadh and {Song}, Xiaodan and {Demmel}, James and {Keutzer}, Kurt and {Hsieh}, Cho-Jui},
	year         = 2020,
	month        = {Apr},
	booktitle    = {Proceedings of the 8th International Conference on Learning Representations},
	url          = {https://ui.adsabs.harvard.edu/abs/2019arXiv190400962Y}
}
@article{radam,
	title        = {{On the Variance of the Adaptive Learning Rate and Beyond}},
	author       = {{Liu}, Liyuan and {Jiang}, Haoming and {He}, Pengcheng and {Chen}, Weizhu and {Liu}, Xiaodong and {Gao}, Jianfeng and {Han}, Jiawei},
	year         = 2019,
	month        = {Aug},
	journal      = {CoRR},
	pages        = {arXiv:1908.03265},
	keywords     = {Computer Science - Machine Learning, Computer Science - Computation and Language, Statistics - Machine Learning},
	eid          = {arXiv:1908.03265},
	archiveprefix = {arXiv},
	eprint       = {1908.03265},
	primaryclass = {cs.LG},
	adsurl       = {https://ui.adsabs.harvard.edu/abs/2019arXiv190803265L},
	adsnote      = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{multi-hop-qa,
	title        = {Do Multi-hop Readers Dream of Reasoning Chains?},
	author       = {Wang, Haoyu  and Yu, Mo  and Guo, Xiaoxiao  and Das, Rajarshi  and Xiong, Wenhan  and Gao, Tian},
	year         = 2019,
	booktitle    = {Proceedings of the 2nd Workshop on Machine Reading for Question Answering},
	address      = {Hong Kong, China},
	pages        = {91--97},
	doi          = {10.18653/v1/D19-5813},
	url          = {https://www.aclweb.org/anthology/D19-5813}
}
@inproceedings{sentiment_analysis_bert,
	title        = {Utilizing {BERT} for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence},
	author       = {Sun, Chi  and Huang, Luyao  and Qiu, Xipeng},
	year         = 2019,
	booktitle    = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies},
	address      = {Minneapolis, MN},
	pages        = {380--385},
	doi          = {10.18653/v1/N19-1035},
	url          = {https://www.aclweb.org/anthology/N19-1035}
}
@inproceedings{so_embeddings,
	title        = {Word Embeddings for the Software Engineering Domain},
	author       = {V. {Efstathiou} and C. {Chatzilenas} and D. {Spinellis}},
	year         = 2018,
	month        = {May},
	booktitle    = {IEEE/ACM 15th International Conference on Mining Software Repositories},
	address      = {Gothenburg, Sweden},
	pages        = {38--41},
	issn         = {2574-3848},
	url          = {https://ieeexplore.ieee.org/document/8595174}
}
@inproceedings{treecnn-spc,
	title        = {Natural Language Inference by Tree-Based Convolution and Heuristic Matching},
	author       = {Mou, Lili  and Men, Rui  and Li, Ge  and Xu, Yan  and Zhang, Lu  and Yan, Rui  and Jin, Zhi},
	year         = 2016,
	month        = aug,
	booktitle    = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics},
	address      = {Berlin, Germany},
	pages        = {130--136},
	doi          = {10.18653/v1/P16-2022},
	url          = {https://www.aclweb.org/anthology/P16-2022}
}
@inproceedings{sin_rnn_lstm-spc,
	title        = {A Sentence Interaction Network for Modeling Dependence between Sentences},
	author       = {Liu, Biao  and Huang, Minlie},
	year         = 2016,
	month        = aug,
	booktitle    = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics},
	address      = {Berlin, Germany},
	pages        = {558--567},
	doi          = {10.18653/v1/P16-1053},
	url          = {https://www.aclweb.org/anthology/P16-1053}
}
@inproceedings{char-bilstm-spc,
	title        = {Character-Based Neural Networks for Sentence Pair Modeling},
	author       = {Lan, Wuwei  and Xu, Wei},
	year         = 2018,
	month        = jun,
	booktitle    = {Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
	address      = {New Orleans, Louisiana},
	pages        = {157--163},
	doi          = {10.18653/v1/N18-2025},
	url          = {https://www.aclweb.org/anthology/N18-2025},
	abstract     = {Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference. Most state-of-the-art neural models for these tasks rely on pretrained word embedding and compose sentence-level semantics in varied ways; however, few works have attempted to verify whether we really need pretrained embeddings in these tasks. In this paper, we study how effective subword-level (character and character n-gram) representations are in sentence pair modeling. Though it is well-known that subword models are effective in tasks with single sentence input, including language modeling and machine translation, they have not been systematically studied in sentence pair modeling tasks where the semantic and string similarities between texts matter. Our experiments show that subword models without any pretrained word embedding can achieve new state-of-the-art results on two social media datasets and competitive results on news data for paraphrase identification.}
}
@inproceedings{overview-spc,
	title        = {Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering},
	author       = {Lan, Wuwei  and Xu, Wei},
	year         = 2018,
	month        = aug,
	booktitle    = {Proceedings of the 27th International Conference on Computational Linguistics},
	publisher    = {Association for Computational Linguistics},
	address      = {Santa Fe, New Mexico, USA},
	pages        = {3890--3902},
	url          = {https://www.aclweb.org/anthology/C18-1328},
	abstract     = {In this paper, we analyze several neural network designs (and their variations) for sentence pair modeling and compare their performance extensively across eight datasets, including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks. Although most of these models have claimed state-of-the-art performance, the original papers often reported on only one or two selected datasets. We provide a systematic study and show that (i) encoding contextual information by LSTM and inter-sentence interactions are critical, (ii) Tree-LSTM does not help as much as previously claimed but surprisingly improves performance on Twitter datasets, (iii) the Enhanced Sequential Inference Model is the best so far for larger datasets, while the Pairwise Word Interaction Model achieves the best performance when less data is available. We release our implementations as an open-source toolkit.}
}
@article{abcnn-spc,
	title        = {{ABCNN}: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs},
	author       = {Yin, Wenpeng  and Schutze, Hinrich  and Xiang, Bing  and Zhou, Bowen},
	year         = 2016,
	journal      = {Transactions of the Association for Computational Linguistics},
	volume       = 4,
	pages        = {259--272},
	doi          = {10.1162/tacl\_a\_00097},
	url          = {https://www.aclweb.org/anthology/Q16-1019},
	abstract     = {How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence{'}s representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) The ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNNs; thus, the representation of each sentence takes into consideration its counterpart. These interdependent sentence pair representations are more powerful than isolated sentence representations. (iii) ABCNNs achieve state-of-the-art performance on AS, PI and TE tasks. We release code at: https://github.com/yinwenpeng/Answer{\_}Selection.}
}
@inproceedings{qa_biomedical_self,
	title        = {Question Answering in the Biomedical Domain},
	author       = {Nguyen, Vincent},
	year         = 2019,
	month        = jul,
	booktitle    = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop},
	address      = {Florence, Italy},
	pages        = {54--63},
	doi          = {10.18653/v1/P19-2008},
	url          = {https://www.aclweb.org/anthology/P19-2008}
}
@inproceedings{mt-dnn,
	title        = {Multi-Task Deep Neural Networks for Natural Language Understanding},
	author       = {Liu, Xiaodong  and He, Pengcheng  and Chen, Weizhu  and Gao, Jianfeng},
	year         = 2019,
	month        = jul,
	booktitle    = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
	address      = {Florence, Italy},
	pages        = {4487--4496},
	url          = {https://www.aclweb.org/anthology/P19-1441}
}
@inproceedings{bugfix_bert,
	title        = {Towards Standardizing and Improving Classification of Bug-Fix Commits},
	author       = {S. {Zafar} and M. Z. {Malik} and G. S. {Walia}},
	year         = 2019,
	booktitle    = {2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement},
	pages        = {1--6}
}
@inproceedings{so_entity-acl,
	title        = {Code and Named Entity Recognition in StackOverflow},
	author       = {Jeniya Tabassum and Mounica Maddela and Wei Xu and Alan Ritter},
	booktitle    = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}
}
@book{Aho:72,
	title        = {The Theory of Parsing, Translation and Compiling},
	author       = {Alfred V. Aho and Jeffrey D. Ullman},
	year         = 1972,
	publisher    = {Prentice-Hall},
	address      = {Englewood Cliffs, NJ},
	volume       = 1
}
@book{APA:83,
	title        = {Publications Manual},
	author       = {{American Psychological Association}},
	year         = 1983,
	publisher    = {American Psychological Association},
	address      = {Washington, DC}
}
@article{Chandra:81,
	title        = {Alternation},
	author       = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year         = 1981,
	journal      = {Journal of the Association for Computing Machinery},
	volume       = 28,
	number       = 1,
	pages        = {114--133},
	doi          = {10.1145/322234.322243}
}
@inproceedings{andrew2007scalable,
	title        = {Scalable training of {L1}-regularized log-linear models},
	author       = {Andrew, Galen and Gao, Jianfeng},
	year         = 2007,
	booktitle    = {Proceedings of the 24th International Conference on Machine Learning},
	pages        = {33--40}
}
@book{Gusfield:97,
	title        = {Algorithms on Strings, Trees and Sequences},
	author       = {Dan Gusfield},
	year         = 1997,
	publisher    = {Cambridge University Press},
	address      = {Cambridge, UK}
}
@article{rasooli-tetrault-2015,
	title        = {Yara Parser: {A} Fast and Accurate Dependency Parser},
	author       = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
	year         = 2015,
	journal      = {CoRR},
	volume       = {arXiv:1503.06733},
	url          = {http://arxiv.org/abs/1503.06733},
	note         = {version 2}
}
@article{fastai,
	title        = {Fastai: A Layered {API} for Deep Learning},
	author       = {Howard, Jeremy and Gugger, Sylvain},
	year         = 2020,
	month        = {02},
	journal      = {Information},
	volume       = 11,
	pages        = 108,
	doi          = {10.3390/info11020108}
}
@inproceedings{textual_entailment_original,
	title        = {PROBABILISTIC TEXTUAL ENTAILMENT: GENERIC APPLIED MODELING OF LANGUAGE VARIABILITY},
	author       = {I. Dagan and Oren Glickman},
	year         = 2004,
	booktitle    = {PASCAL Workshop on Learning Methods for Text Understanding and Mining},
	address      = {Grenoble, France},
	url          = {http://www2.denizyuret.com/ref/dagan/dagan\_glickman\_pascal\_2004.pdf}
}
@inbook{svm_textual_entailment,
	title        = {Learning Textual Entailment Using SVMs and String Similarity Measures},
	author       = {Malakasiotis, Prodromos and Androutsopoulos, Ion},
	year         = 2007,
	booktitle    = {Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing},
	publisher    = {Association for Computational Linguistics},
	address      = {USA},
	pages        = {42–47},
	url          = {https://www.aclweb.org/anthology/W07-1407.pdf},
	numpages     = 6
}
@inproceedings{textual_entailment_machine_learning,
	title        = {Machine  Learning Experiments   for  Textual  Entailment},
	author       = {Diana Inkpen and Darren Kipp and Vivi Nastase},
	year         = 2006,
	booktitle    = {Proceedings of the second RTE Challenge},
	address      = {Venice, Italy},
	url          = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.596.8949\&rep=rep1\&type=pdf}
}
@inproceedings{attention_flow,
	title        = {Quantifying Attention Flow in Transformers},
	author       = {Abnar, Samira  and Zuidema, Willem},
	year         = 2020,
	month        = jul,
	booktitle    = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
	address      = {Online},
	pages        = {4190--4197},
	doi          = {10.18653/v1/2020.acl-main.385},
	url          = {https://www.aclweb.org/anthology/2020.acl-main.385}
}
@misc{sota_snli,
	title        = {Self-Explaining Structures Improve NLP Models},
	author       = {Zijun Sun and Chun Fan and Qinghong Han and Xiaofei Sun and Yuxian Meng and Fei Wu and Jiwei Li},
	year         = 2020,
	eprint       = {2012.01786},
	archiveprefix = {arXiv}
}
@inbook{decision_tree_te,
	title        = {Textual Entailment Recognition Using a Linguistically–Motivated Decision Tree Classifier},
	author       = {Newman, Eamonn and Stokes, Nicola and Dunnion, John and Carthy, Joe},
	year         = 2006,
	month        = {07},
	volume       = 3944,
	pages        = {372--384},
	doi          = {10.1007/11736790\_21},
	isbn         = {978-3-540-33427-9}
}
@article{big_name,
	title        = {Generation and evaluation of artificial mental health records for Natural Language Processing},
	author       = {Ive, Julia and Viani, Natalia and Kam, Joyce and Yin, Lucia and Verma, Somain and Puntis, Stephen and Cardinal, Rudolf N. and Roberts, Angus and Stewart, Robert and Velupillai, Sumithra},
	year         = 2020,
	month        = {May},
	day          = 14,
	journal      = {Nature Partner Journal Digital Medicine},
	volume       = 3,
	number       = 1,
	pages        = 69,
	url          = {https://doi.org/10.1038/s41746-020-0267-x}
}
@inproceedings{Roberts:Simpson:2015,
	title        = {Overview of the {TREC} 2015 Clinical Decision Support Track},
	author       = {Kirk Roberts and Matthew Simpson and Ellen Voorhees and William Hersh},
	year         = 2015,
	booktitle    = {TREC},
	address      = {Gaithersburg, MD},
    url = {https://trec.nist.gov/pubs/trec24/papers/Overview-CL.pdf}
}
@inproceedings{Karimi:Martinez:Ghodke:2011,
	title        = {Search for Medical Records: {NICTA} at {TREC} 2011 Medical Track},
	author       = {S. Karimi and D. Martinez and S. Ghodke and L. Zhang and H. Suominen and L. Cavedon},
	year         = 2011,
	booktitle    = {TREC}
}
@inproceedings{Karimi:Scholer:2011,
	title        = {Systematic Reviews: A Complex Search Episode for Evidence Based Policy and Practice},
	author       = {Sarvnaz Karimi and Falk Scholer},
	year         = 2011,
	booktitle    = {SIGIR 2011 Workshop on "entertain me": Supporting Complex Search Tasks},
	pages        = 7
}
@article{Karimi:Pohl:Scholer:2010,
	title        = {Boolean versus ranked querying for biomedical systematic reviews},
	author       = {Sarvnaz Karimi and Stefan Pohl and Falk Scholer and Lawrence Cavedon and Justin Zobel},
	year         = 2010,
	journal      = {BMC Medical Informatics and Decision Making},
	volume       = 10,
	number       = 58
}
@article{Caoa:Ciminob:2010,
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% FIX THIS!
@inproceedings{Raja:Dasot:Goyal:2016,
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	booktitle    = {SIGKDD},
	location     = {Halifax, Canada},
	publisher    = {Association for Computing Machinery},
	address      = {New York, NY, USA},
	pages        = {1557–1565},
	doi          = {10.1145/3097983.3098011},
	numpages     = 9,
	keywords     = {user experience, operational e-commerce search system, cascade ranking, effectiveness and efficiency}
}
@article{ellen2020trecCovid,
	title        = {{TREC-COVID}: Constructing a Pandemic Information Retrieval Test Collection},
	author       = {Voorhees, Ellen and Tasmeer, Alam and Dina, Demner-Fushman and William, Hersh and Kyle Lo},
	year         = 2020,
	journal      = {ACM SIGIR Forum},
	volume       = 54,
	number       = 1,
	pages        = {1--12},
    url = {https://dl.acm.org/doi/10.1145/3451964.3451965}
}
@article{jon2020-covid-19-misinformation,
	title        = {Susceptibility to misinformation about COVID-19 around the world},
	author       = {Roozenbeek Jon and Schneider Claudia and Dryhurst Sarah and Kerr John and Freeman Alexandra and Recchia Gabriel and van der Bles Anne Marthe and van der Linden Sander},
	year         = 2020,
	journal      = {R. Soc. Open. Sci.},
	volume       = 7,
	issue        = 10,
    url = {https://royalsocietypublishing.org/doi/10.1098/rsos.201199}
}
@article{Hussain2018,
	title        = {The Anti-vaccination Movement: A Regression in Modern Medicine},
	author       = {Hussain, Azhar and Ali, Syed and Ahmed, Madiha and Hussain, Sheharyar},
	year         = 2018,
	month        = {Jul},
	day          = {03},
	journal      = {Cureus},
	volume       = 10,
	number       = 7,
	pages        = {e2919-e2919},
	doi          = {10.7759/cureus.2919},
	issn         = {2168-8184},
	url          = {https://pubmed.ncbi.nlm.nih.gov/30186724},
	note         = {PMC6122668[pmcid]}
}
@article{Abacha2020ConsumerQA,
	title        = {Consumer health information and question answering: helping consumers find answers to their health-related information needs},
	author       = {Demner-Fushman, Dina and Mrabet, Yassine and Ben Abacha, Asma},
	year         = 2020,
	month        = {Feb},
	day          = {01},
	journal      = {JAMIA},
	publisher    = {Oxford University Press},
	volume       = 27,
	number       = 2,
	pages        = {194--201},
	doi          = {10.1093/jamia/ocz152},
	issn         = {1527-974X},
	url          = {https://pubmed.ncbi.nlm.nih.gov/31592532},
	note         = {31592532[pmid]},
	language     = {eng}
}
@inproceedings{nguyen-etal-2020-pandemic,
	title        = {Pandemic Literature Search: Finding Information on {COVID}-19},
	author       = {Nguyen, Vincent  and Rybinski, Maciej  and Karimi, Sarvnaz  and Xing, Zhenchang},
	year         = 2020,
	month        = dec,
	booktitle    = {Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association},
	pages        = {92--97}
}
@inproceedings{queries_vs_questions,
	title        = {Questions vs. Queries in Informational Search Tasks},
	author       = {White, Ryen W. and Richardson, Matthew and Yih, Wen-tau},
	year         = 2015,
	booktitle    = {Proceedings of the 24th International Conference on World Wide Web},
	location     = {Florence, Italy},
	publisher    = {Association for Computing Machinery},
	address      = {New York, NY, USA},
	series       = {WWW '15 Companion},
	pages        = {135–136},
	doi          = {10.1145/2740908.2742769},
	isbn         = 9781450334730,
	url          = {https://doi.org/10.1145/2740908.2742769},
	numpages     = 2,
	keywords     = {query formulation, informational search, natural language queries}
}
@article{Fogel:Clinical:2018,
	title        = {Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review},
	author       = {David B Fogel},
	year         = 2018,
	journal      = {Contemporary clinical trials communications},
	pages        = {156--164}
}
@article{Sackett:Rosenberg:BMJ:1996,
	title        = {Evidence based medicine: what it is and what it isn't},
	author       = {David L Sackett and William MC Rosenberg and JA Muir Gray and R Brian Haynes and W Scott Richardson},
	year         = 1996,
	journal      = {The BMJ}
}
@article{Wang:Zhang:Lancet:2020,
	title        = {Remdesivir in adults with severe {COVID-19}: a randomised, double-blind, placebo-controlled, multicentre trial},
	author       = {Yeming Wang and Dingyu Zhang and Guanhua Du and Ronghui Du and Jianping Zhao and Yang Jin and Shouzhi Fu and Ling Gao and Zhenshun Cheng and Qiaofa Lu},
	year         = 2020,
	journal      = {The Lancet},
	pages        = {1569--1578}
}
@article{Shen:Liu:JMIR:2021,
	title        = {Family History Extraction From Synthetic Clinical Narratives Using Natural Language Processing: Overview and Evaluation of a Challenge Data Set and Solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) Competition},
	author       = {Feichen Shen and Sijia Liu and Sunyang Fu and Yanshan Wang and Sam Henry and Ozlem Uzuner and Hongfang Liu},
	year         = 2021,
	journal      = {JMIR Medical Informatics},
	pages        = {e24008}
}
@book{Sutton:McCallum:2007,
	title        = {An introduction to conditional random fields for relational learning},
	author       = {Charles Sutton and Andrew McCallum},
	year         = 2007,
	booktitle    = {Introduction to statistical relational learning},
	publisher    = {The MIT Press}
}
@inproceedings{Gururangan:Marasovic:ACL:2020,
	title        = {Don't Stop Pretraining: Adapt Language Models to Domains and Tasks},
	author       = {Suchin Gururangan and Ana Marasovic and Swabha Swayamdipta and Kyle Lo and Iz Beltagy and Doug Downey and Noah A Smith},
	year         = 2020,
	booktitle    = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
	address      = {Online},
	pages        = {8342--8360}
}
@inproceedings{Dai:Karimi:EMNLP:2020,
	title        = {Cost-effective Selection of Pretraining Data: A Case Study of Pretraining {BERT} on Social Media},
	author       = {Xiang Dai and Sarvnaz Karimi and Ben Hachey and Cecile Paris},
	year         = 2020,
	booktitle    = {Findings of the Association for Computational Linguistics: EMNLP 2020},
	address      = {Online},
	pages        = {1675--1681}
}
@inproceedings{Alsentzer:Murphy:ClinicalNLP:2019,
	title        = {Publicly available clinical {BERT} embeddings},
	author       = {Emily Alsentzer and John R Murphy and Willie Boag and Wei-Hung Weng and Di Jin and Tristan Naumann and Matthew McDermott},
	year         = 2019,
	booktitle    = {Proceedings of the 2nd Clinical Natural Language Processing Workshop},
	address      = {Minneapolis, Minnesota},
	pages        = {72--78}
}
@inproceedings{Pruksachatkun:Phang:ACL:2020,
	title        = {Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?},
	author       = {Yada Pruksachatkun and Jason Phang and Haokun Liu and Phu Mon Htut and Xiaoyi Zhang and Richard Yuanzhe Pang and Clara Vania and Katharina Kann and Samuel R Bowman},
	year         = 2020,
	booktitle    = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
	address      = {Online},
	pages        = {5231--5247}
}
@article{Dogan:Leaman:JBI:2014,
	title        = {{NCBI} disease corpus: a resource for disease name recognition and concept normalization},
	author       = {Rezarta Islamaj Do{\u{g}}an and Robert Leaman and Zhiyong Lu},
	year         = 2014,
	journal      = {Journal of biomedical informatics},
	pages        = {1--10}
}
@article{Cer:Yang:arXiv:2018,
	title        = {Universal sentence encoder},
	author       = {Daniel Cer and Yinfei Yang and Sheng-yi Kong and Nan Hua and Nicole Limtiaco and Rhomni St John and Noah Constant and Mario Guajardo-Cespedes and Steve Yuan and Chris Tar},
	year         = 2018,
	journal      = {arXiv preprint arXiv:1803.11175}
}
@article{Henry:Wang:2020,
	title        = {The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task on clinical concept normalization for clinical records},
	author       = {Sam Henry and Yanshan Wang and Feichen Shen and Ozlem Uzuner},
	year         = 2020,
	journal      = {Journal of the American Medical Informatics Association},
	pages        = {1529–1537}
}
@article{Loos:Bouchard:InternMed:2003,
	title        = {Obesity–is it a genetic disorder?},
	author       = {Ruth JF Loos and Claude Bouchard},
	year         = 2003,
	journal      = {Journal of internal medicine},
	pages        = {401--425}
}
@article{Bullo:Garcia-Aloy:PrevMed:2011,
	title        = {Association between a healthy lifestyle and general obesity and abdominal obesity in an elderly population at high cardiovascular risk},
	author       = {M\`{o}nica Bullo and Mar Garcia-Aloy and Miguel A Martinez-Gonzalez and Dolores Corella and Joan D Fernandez-Ballart and Miquel Fiol and Enrique Gomez-Gracia and Ramon Estruch and Manuel Ortega-Calvo and Silvia Francisco},
	year         = 2011,
	journal      = {Preventive medicine},
	pages        = {155--161}
}
@article{consumerExpertQuestSep2011,
	title        = {Toward automated consumer question answering: Automatically separating consumer questions from professional questions in the healthcare domain},
	author       = {Feifan Liu and Lamont D. Antieau and Hong Yu},
	year         = 2011,
	journal      = {Journal of Biomedical Informatics},
	volume       = 44,
	number       = 6,
	pages        = {1032--1038},
	doi          = {https://doi.org/10.1016/j.jbi.2011.08.008},
	issn         = {1532-0464},
	url          = {http://www.sciencedirect.com/science/article/pii/S1532046411001353}
}
@inproceedings{jimmyHealthCard2019,
	title        = {Health Card Retrieval for Consumer Health Search: An Empirical Investigation of Methods},
	author       = {Jimmy and Zuccon, Guido and Koopman, Bevan and Demartini, Gianluca},
	year         = 2019,
	booktitle    = {CIKM},
	location     = {Beijing, China},
	address      = {New York, NY, USA},
	pages        = {2405–2408},
	doi          = {10.1145/3357384.3358128},
	isbn         = 9781450369763,
	url          = {https://doi.org/10.1145/3357384.3358128},
	numpages     = 4
}
@inproceedings{diego-et-al-bioasq-2020,
	title        = {Pandemic Literature Search: Finding Information on {COVID}-19},
	author       = {Molla, Diego, and Jones, Christopher and Nguyen, Vincent},
	year         = 2020,
	month        = sep,
	booktitle    = {Working Notes of CLEF 2020},
	publisher    = {CLEF 2020},
	address      = {Thessaloniki, Greece},
	url          = {http://ceur-ws.org/Vol-2696/paper\_119.pdf}
}
@article{VanGoethem2020,
	title        = {Rapid establishment of a national surveillance of COVID-19 hospitalizations in Belgium},
	author       = {Van Goethem, Nina and Vilain, Aline and Wyndham-Thomas, Chloe and Deblonde, Jessika and Bossuyt, Nathalie and Lernout, Tinne and Rebolledo Gonzalez, Javiera and Quoilin, Sophie and Melis, Vincent and Van Beckhoven, Dominique},
	year         = 2020,
	month        = {Nov},
	day          = 18,
	journal      = {Archives of Public Health},
	volume       = 78,
	number       = 1,
	pages        = 121,
	doi          = {10.1186/s13690-020-00505-z},
	issn         = {2049-3258},
	url          = {https://doi.org/10.1186/s13690-020-00505-z},
	abstract     = {In response to the COVID-19 epidemic, caused by a novel coronavirus, it was of great importance to rapidly collect as much accurate information as possible in order to characterize the public health threat and support the health authorities in its management. Hospital-based surveillance is paramount to monitor the severity of a disease in the population.}
}
@inproceedings{trec_2007,
	title        = {Overview of the TREC 2007 Question Answering Track.},
	author       = {Dang, Hoa Trang and Kelly, Diane and Lin, Jimmy J},
	year         = 2007,
	booktitle    = {Proceedings of the Sixteenth Text REtrieval Conference (TREC 2007)}
}
@inproceedings{trec_2001,
	title        = {Overview of the TREC-9 question answering track},
	author       = {Voorhees, Ellen M},
	year         = 2001,
	booktitle    = {Proceedings of the Ninth Text REtrieval Conference (TREC-9},
	organization = {Citeseer}
}
@inproceedings{baseball_qa,
	title        = {Baseball: an automatic question-answerer},
	author       = {Green Jr, Bert F and Wolf, Alice K and Chomsky, Carol and Laughery, Kenneth},
	year         = 1961,
	booktitle    = {Papers presented at the May 9-11, 1961, western joint IRE-AIEE-ACM computer conference},
	pages        = {219--224}
}
@article{view_from_here_overview_2001,
	title        = {Natural Language Question Answering: The View from Here},
	author       = {Hirschman, Lynette and Gaizauskas, Rob},
	year         = 2001,
	month        = 12,
	journal      = {Natural Language Engineering},
	volume       = 7,
	pages        = {275--300},
	doi          = {10.1017/S1351324901002807}
}
@inproceedings{visual_question_answering_vqa_2015,
	title        = {Vqa: Visual question answering},
	author       = {Antol, Stanislaw and Agrawal, Aishwarya and Lu, Jiasen and Mitchell, Margaret and Batra, Dhruv and Lawrence Zitnick, C and Parikh, Devi},
	year         = 2015,
	booktitle    = {Proceedings of the IEEE international conference on computer vision},
	pages        = {2425--2433}
}
% Needs country
@inproceedings{freebase_qa_kb_2014,
	title        = {Information extraction over structured data: Question answering with freebase},
	author       = {Yao, Xuchen and Van Durme, Benjamin},
	year         = 2014,
	booktitle    = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
	pages        = {956--966}
}
@inproceedings{semantic_role_qa_2007,
	title        = {Using Semantic Roles to Improve Question Answering},
	author       = {Shen, Dan  and Lapata, Mirella},
	year         = 2007,
	month        = jun,
	booktitle    = {Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning ({EMNLP}-{C}o{NLL})},
	address      = {Prague, Czech Republic},
	pages        = {12--21},
	url          = {https://www.aclweb.org/anthology/D07-1002}
}
@inproceedings{semantic_parsing_qa_2014,
	title        = {Semantic Parsing for Single-Relation Question Answering},
	author       = {Yih, Wen-tau  and  He, Xiaodong  and  Meek, Christopher},
	year         = 2014,
	month        = {June},
	booktitle    = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
	address      = {Baltimore, Maryland},
	pages        = {643--648},
	url          = {http://www.aclweb.org/anthology/P/P14/P14-2105}
}
@article{Tricco2018,
	title        = {Engaging policy-makers, health system managers, and policy analysts in the knowledge synthesis process: a scoping review},
	author       = {Tricco, Andrea C. and Zarin, Wasifa and Rios, Patricia and Nincic, Vera and Khan, Paul A. and Ghassemi, Marco and Diaz, Sanober and Pham, Ba' and Straus, Sharon E. and Langlois, Etienne V.},
	year         = 2018,
	month        = {Feb},
	day          = 12,
	journal      = {Implementation Science},
	volume       = 13,
	number       = 1,
	pages        = 31,
	doi          = {10.1186/s13012-018-0717-x},
	issn         = {1748-5908},
	url          = {https://doi.org/10.1186/s13012-018-0717-x}
}
@article{Loeb2019,
	title        = {Dissemination of Misinformative and Biased Information about Prostate Cancer on YouTube},
	author       = {Loeb, Stacy and Sengupta, Shomik and Butaney, Mohit and Macaluso Joseph, and Czarniecki, Stefan W. and Robbins, Rebecca and Braithwaite, R. Scott and Gao, Lingshan and Byrne, Nataliya and Walter, Dawn and Langford, Aisha},
	year         = 2019,
	month        = {Apr},
	day          = {01},
	journal      = {European Urology},
	publisher    = {Elsevier},
	volume       = 75,
	number       = 4,
	pages        = {564--567},
	doi          = {10.1016/j.eururo.2018.10.056},
	issn         = {0302-2838},
	url          = {https://doi.org/10.1016/j.eururo.2018.10.056},
}

@article{Fontelo2005,
	title        = {ask MEDLINE: a free-text, natural language query tool for MEDLINE/PubMed},
	author       = {Fontelo, Paul and Liu, Fang and Ackerman, Michael},
	year         = 2005,
	month        = {Mar},
	day          = 10,
	journal      = {BMC Medical Informatics and Decision Making},
	volume       = 5,
	number       = 1,
	pages        = 5,
	doi          = {10.1186/1472-6947-5-5},
	issn         = {1472-6947},
	url          = {https://doi.org/10.1186/1472-6947-5-5},
	abstract     = {Plain language search tools for MEDLINE/PubMed are few. We wanted to develop a search tool that would allow anyone using a free-text, natural language query and without knowing specialized vocabularies that an expert searcher might use, to find relevant citations in MEDLINE/PubMed. This tool would translate a question into an efficient search.}
}
@string{AMSTrans = "American Mathematical Society Translations" }
@string{GiornaleMath = "Giorn. Mat." }
% didn't find in AMS MR., ibid.
 %IEEE
@string{QuartMath = "Quarterly Journal of Mathematics, Oxford, Series (2)" }
@string{HoltRinehartWinston = "Holt, Rinehart and Winston" }
@article{Abril07,
	title        = {The patent holder's dilemma: Buy, sell, or troll?},
	author       = {Patricia S. Abril and Robert Plant},
	year         = 2007,
	month        = jan,
	journal      = {Communications of the ACM},
	volume       = 50,
	number       = 1,
	pages        = {36--44},
	doi          = {10.1145/1188913.1188915},
	url          = {http://doi.acm.org/10.1145/1219092.1219093},
	note         = {}
}
@article{Cohen07,
	title        = {Deciding equivalances among conjunctive aggregate queries},
	author       = {Sarah Cohen and Werner Nutt and Yehoshua Sagic},
	year         = 2007,
	month        = apr,
	journal      = JACM,
	volume       = 54,
	number       = 2,
	doi          = {10.1145/1219092.1219093},
	url          = {http://doi.acm.org/10.1145/1219092.1219093},
	note         = {},
	articleno    = 5,
	numpages     = 50,
	acmid        = 1219093
}
@periodical{JCohen96,
	title        = {Special issue: Digital Libraries},
	year         = 1996,
	month        = nov,
	journal      = CACM,
	volume       = 39,
	number       = 11,
	key          = {Cohen},
	editor       = {Jacques Cohen}
}
@book{Kosiur01,
	title        = {Understanding Policy-Based Networking},
	author       = {David Kosiur},
	year         = 2001,
	month        = {},
	publisher    = {Wiley},
	address      = {New York, NY},
	series       = {},
	volume       = {},
	number       = {},
	note         = {},
	edition      = {2nd.},
	editor       = {}
}
@book{Harel79,
	title        = {First-Order Dynamic Logic},
	author       = {David Harel},
	year         = 1979,
	month        = {},
	publisher    = {Springer-Verlag},
	address      = {New York, NY},
	series       = {Lecture Notes in Computer Science},
	volume       = 68,
	number       = {},
	pages        = {},
	doi          = {10.1007/3-540-09237-4},
	url          = {http://dx.doi.org/10.1007/3-540-09237-4},
	note         = {},
	editor       = {},
	subtitle     = {The book subtitle},
	edition      = {1st.},
	chapter      = {},
	type         = {}
}
%
% incollection (has an editor, title, and possibly a booktitle)
@incollection{Spector90,
	title        = {Achieving application requirements},
	author       = {Asad Z. Spector},
	year         = 1990,
	month        = {},
	booktitle    = {Distributed Systems},
	publisher    = {ACM Press},
	address      = {New York, NY},
	series       = {},
	volume       = {},
	number       = {},
	pages        = {19--33},
	doi          = {10.1145/90417.90738},
	url          = {http://doi.acm.org/10.1145/90417.90738},
	note         = {},
	edition      = {2nd.},
	chapter      = {},
	editor       = {Sape Mullender},
	type         = {}
}
% incollection (has an editor, title, and possibly a booktitle)
@incollection{Douglass98,
	title        = {Statecarts in use: structured analysis and object-orientation},
	author       = {Bruce P. Douglass and David Harel and Mark B. Trakhtenbrot},
	year         = 1998,
	month        = {},
	booktitle    = {Lectures on Embedded Systems},
	publisher    = {Springer-Verlag},
	address      = {London},
	series       = {Lecture Notes in Computer Science},
	volume       = 1494,
	number       = {},
	pages        = {368--394},
	doi          = {10.1007/3-540-65193-4\_29},
	url          = {http://dx.doi.org/10.1007/3-540-65193-4\_29},
	note         = {},
	chapter      = {},
	editor       = {Grzegorz Rozenberg and Frits W. Vaandrager},
	edition      = {},
	type         = {}
}
@book{Knuth97,
	title        = {The Art of Computer Programming, Vol. 1: Fundamental Algorithms (3rd. ed.)},
	author       = {Donald E. Knuth},
	year         = 1997,
	month        = {},
	publisher    = {Addison Wesley Longman Publishing Co., Inc.},
	address      = {},
	series       = {},
	volume       = {},
	number       = {},
	note         = {},
	edition      = {},
	editor       = {}
}
@book{Knuth98,
	title        = {The Art of Computer Programming},
	author       = {Donald E. Knuth},
	year         = 1998,
	month        = {},
	publisher    = {Addison Wesley Longman Publishing Co., Inc.},
	address      = {},
	series       = {Fundamental Algorithms},
	volume       = 1,
	number       = {},
	doi          = {},
	url          = {},
	note         = {(book)},
	edition      = {3rd},
	editor       = {}
}
%Inbook{Knuth97,
%  author =       "Donald E. Knuth",
%  title =        "The Art of Computer Programming",
%  booktitle =    "the booktitle",
%  edition =      "3",
%  volume =       "1",
%  year =         "1997",
%  publisher =    "Addison Wesley Longman Publishing Co., Inc.",
%  editor =       "",
%  number =       "",
%  series =       "Fundamental Algorithms",
%  type =         "",
%  chapter =      "",
%  pages =        "",
%  address =      "",
%  month =        "",
%  note =         "(inbook)",
%}
%INBOOK{DK:73-inbook-full,
%   author = "Donald E. Knuth",
%   title = "Fundamental Algorithms (inbook w series)",
%   volume = 1,
%   series = "The Art of Computer Programming",
%   publisher = "Addison-Wesley",
%   address = "Reading, Massachusetts",
%   edition = "Second",
%   month = "10~" # jan,
%   year = "1973",
%   type = "Section",
%   chapter = "1.2",
%   pages = "10--119",
%   note = "Full INBOOK entry (w series)",
%}
%INcollection{DK:74-incoll,
%   author = "Donald E. Knuth",
%   title = "Fundamental Algorithms (incoll)",
%   volume = 1,
%   booktitle = "The Art of Computer Programming",
%   publisher = "Addison-Wesley",
%   address = "Reading, Massachusetts",
%   month = "10~" # jan,
%   year = "1974",
%   pages = "10--119",
%   editor = "Bernard Rous",
%   note = "This is a full incoll entry with an editor",
%}
%INcollection{DK:75-incollws,
%   author = "Donald E. Knuth",
%   title = "Fundamental Algorithms (incoll w series)",
%   volume = 1,
%   booktitle = "The Art of Computer Programming",
%   series = "The Art of Computer Programming",
%   publisher = "Addison-Wesley",
%   address = "Reading, Massachusetts",
%   month = "10~" # jan,
%   year = "1975",
%   pages = "10--119",
%   editor = "Bernard Rous",
%   note = "This is a full incoll entry with an editor and series",
%}
@incollection{GM05,
	title        = {Structured Variational Inference Procedures and their Realizations (as incol)},
	author       = {Dan Geiger and Christopher Meek},
	year         = 2005,
	month        = jan,
	booktitle    = {Proceedings of Tenth International Workshop on Artificial Intelligence and Statistics, {\rm The Barbados}},
	publisher    = {The Society for Artificial Intelligence and Statistics},
	editors      = {Z. Ghahramani and R. Cowell}
}
@inproceedings{Smith10,
	title        = {An experiment in bibliographic mark-up: Parsing metadata for XML export},
	author       = {Stan W. Smith},
	year         = 2010,
	month        = {},
	booktitle    = {Proceedings of the 3rd. annual workshop on Librarians and Computers},
	publisher    = {Paparazzi Press},
	address      = {Milan Italy},
	series       = {LAC '10},
	volume       = 3,
	number       = {},
	pages        = {422--431},
	doi          = {99.9999/woot07-S422},
	url          = {http://dx.doi.org/99.0000/woot07-S422},
	note         = {},
	editor       = {Reginald N. Smythe and Alexander Noble},
	organization = {},
	howpublished = {Video}
}
@inproceedings{VanGundy07,
	title        = {Catch me, if you can: Evading network signatures with web-based polymorphic worms},
	author       = {Matthew Van Gundy and Davide Balzarotti and Giovanni Vigna},
	year         = 2007,
	month        = {},
	booktitle    = {Proceedings of the first USENIX workshop on Offensive Technologies},
	publisher    = {USENIX Association},
	address      = {Berkley, CA},
	series       = {WOOT '07},
	volume       = {},
	number       = {},
	pages        = {},
	note         = {},
	articleno    = 7,
	numpages     = 9,
	editor       = {},
	organization = {}
}
@inproceedings{Andler79,
	title        = {Predicate Path expressions},
	author       = {Sten Andler},
	year         = 1979,
	month        = {},
	booktitle    = {Proceedings of the 6th. ACM SIGACT-SIGPLAN symposium on Principles of Programming Languages},
	publisher    = {ACM Press},
	address      = {New York, NY},
	series       = {POPL '79},
	volume       = {},
	number       = {},
	pages        = {226--236},
	doi          = {10.1145/567752.567774},
	url          = {http://doi.acm.org/10.1145/567752.567774},
	note         = {},
	editor       = {},
	organization = {}
}
@techreport{Harel78,
	title        = {LOGICS of Programs: AXIOMATICS and DESCRIPTIVE POWER},
	author       = {David Harel},
	year         = 1978,
	month        = {},
	address      = {Cambridge, MA},
	number       = {TR-200},
	note         = {},
	institution  = {Massachusetts Institute of Technology},
	type         = {MIT Research Lab Technical Report}
}
@mastersthesis{anisi03,
	title        = {Optimal Motion Control of a Ground Vehicle},
	author       = {David A. Anisi},
	year         = 2003,
	school       = {Royal Institute of Technology (KTH), Stockholm, Sweden},
	intitution   = {FOI-R-0961-SE, Swedish Defence Research Agency (FOI)}
}
@phdthesis{Clarkson85,
	title        = {Algorithms for Closest-Point Problems (Computational Geometry)},
	author       = {Kenneth L. Clarkson},
	year         = 1985,
	month        = {},
	address      = {Palo Alto, CA},
	note         = {UMI Order Number: AAT 8506171},
	school       = {Stanford University},
	type         = {}
}
@online{Thornburg01,
	title        = {Introduction to Bayesian Statistics},
	author       = {Harry Thornburg},
	year         = 2001,
	month        = mar,
	url          = {http://ccrma.stanford.edu/~jos/bayes/bayes.html},
	lastaccessed = {March 2, 2005}
}
@online{Ablamowicz07,
	title        = {CLIFFORD: a Maple 11 Package for Clifford Algebra Computations, version 11},
	author       = {Rafal Ablamowicz and Bertfried Fauser},
	year         = 2007,
	url          = {http://math.tntech.edu/rafal/cliff11/index.html},
	lastaccessed = {February 28, 2008}
}
@misc{Poker06,
	title        = {Stats and Analysis},
	author       = {Poker-Edge.Com},
	year         = 2006,
	month        = mar,
	url          = {http://www.poker-edge.com/stats.php},
	lastaccessed = {June 7, 2006}
}
@misc{Obama08,
	title        = {A more perfect union},
	author       = {Barack Obama},
	year         = 2008,
	month        = mar,
	day          = 5,
	url          = {http://video.google.com/videoplay?docid=6528042696351994555},
	note         = {},
	howpublished = {Video},
	lastaccessed = {March 21, 2008}
}
@misc{JoeScientist001,
	title        = {The fountain of youth},
	author       = {Joseph Scientist},
	year         = 2009,
	month        = aug,
	url          = {},
	note         = {Patent No. 12345, Filed July 1st., 2008, Issued Aug. 9th., 2009},
	howpublished = {},
	lastaccessed = {}
}
@article{Lee05,
	title        = {Interview with Bill Kinder: January 13, 2005},
	author       = {Newton Lee},
	year         = 2005,
	month        = {Jan.-March},
	journal      = {Comput. Entertain.},
	volume       = 3,
	number       = 1,
	doi          = {10.1145/1057270.1057278},
	url          = {http://doi.acm.org/10.1145/1057270.1057278},
	note         = {},
	eid          = 4,
	howpublished = {Video}
}
@article{Rous08,
	title        = {The Enabling of Digital Libraries},
	author       = {Bernard Rous},
	year         = 2008,
	month        = jul,
	journal      = {Digital Libraries},
	volume       = 12,
	number       = 3,
	doi          = {},
	url          = {},
	note         = {To appear},
	articleno    = 5,
	howpublished = {}
}
@article{384253,
	title        = {(old) Finding minimum congestion spanning trees},
	author       = {Werneck,, Renato and Setubal,, Jo\~{a}o and da Conceic\~{a}o,, Arlindo},
	year         = 2000,
	journal      = {J. Exp. Algorithmics},
	publisher    = {ACM},
	address      = {New York, NY, USA},
	volume       = 5,
	pages        = 11,
	doi          = {http://doi.acm.org/10.1145/351827.384253},
	issn         = {1084-6654}
}
@article{Werneck:2000:FMC:351827.384253,
	title        = {(new) Finding minimum congestion spanning trees},
	author       = {Werneck, Renato and Setubal, Jo\~{a}o and da Conceic\~{a}o, Arlindo},
	year         = 2000,
	month        = dec,
	journal      = {J. Exp. Algorithmics},
	publisher    = {ACM},
	address      = {New York, NY, USA},
	volume       = 5,
	doi          = {10.1145/351827.384253},
	issn         = {1084-6654},
	url          = {http://portal.acm.org/citation.cfm?id=351827.384253},
	articleno    = 11,
	acmid        = 384253
}
@article{1555162,
	title        = {(old) Distributed data source verification in wireless sensor networks},
	author       = {Conti, Mauro and Di Pietro, Roberto and Mancini, Luigi V. and Mei, Alessandro},
	year         = 2009,
	journal      = {Inf. Fusion},
	publisher    = {Elsevier Science Publishers B. V.},
	address      = {Amsterdam, The Netherlands, The Netherlands},
	volume       = 10,
	number       = 4,
	pages        = {342--353},
	doi          = {http://dx.doi.org/10.1016/j.inffus.2009.01.002},
	issn         = {1566-2535}
}
@article{Conti:2009:DDS:1555009.1555162,
	title        = {(new) Distributed data source verification in wireless sensor networks},
	author       = {Conti, Mauro and Di Pietro, Roberto and Mancini, Luigi V. and Mei, Alessandro},
	year         = 2009,
	month        = oct,
	journal      = {Inf. Fusion},
	publisher    = {Elsevier Science Publishers B. V.},
	address      = {Amsterdam, The Netherlands, The Netherlands},
	volume       = 10,
	number       = 4,
	pages        = {342--353},
	doi          = {10.1016/j.inffus.2009.01.002},
	issn         = {1566-2535},
	url          = {http://portal.acm.org/citation.cfm?id=1555009.1555162},
	numpages     = 12,
	acmid        = 1555162,
	keywords     = {Clone detection, Distributed protocol, Securing data fusion, Wireless sensor networks}
}
@inproceedings{Li:2008:PUC:1358628.1358946,
	title        = {Portalis: using competitive online interactions to support aid initiatives for the homeless},
	author       = {Li, Cheng-Lun and Buyuktur, Ayse G. and Hutchful, David K. and Sant, Natasha B. and Nainwal, Satyendra K.},
	year         = 2008,
	booktitle    = {CHI '08 extended abstracts on Human factors in computing systems},
	location     = {Florence, Italy},
	publisher    = {ACM},
	address      = {New York, NY, USA},
	pages        = {3873--3878},
	doi          = {10.1145/1358628.1358946},
	isbn         = {978-1-60558-012-X},
	url          = {http://portal.acm.org/citation.cfm?id=1358628.1358946},
	numpages     = 6,
	acmid        = 1358946,
	keywords     = {cscw, distributed knowledge acquisition, incentive design, online games, recommender systems, reputation systems, user studies, virtual community}
}
@book{Hollis:1999:VBD:519964,
	title        = {Visual Basic 6: Design, Specification, and Objects with Other},
	author       = {Hollis, Billy S.},
	year         = 1999,
	publisher    = {Prentice Hall PTR},
	address      = {Upper Saddle River, NJ, USA},
	isbn         = {0130850845},
	edition      = {1st}
}
@book{Goossens:1999:LWC:553897,
	title        = {The  Latex Web Companion: Integrating TEX, HTML, and XML},
	author       = {Goossens, Michel and Rahtz, S. P. and Moore, Ross and Sutor, Robert S.},
	year         = 1999,
	publisher    = {Addison-Wesley Longman Publishing Co., Inc.},
	address      = {Boston, MA, USA},
	isbn         = {0201433117},
	edition      = {1st}
}
% need to test genres for errant isbn output
% techreport
@techreport{897367,
	title        = {Vertex Types in Book-Embeddings},
	author       = {Buss, Jonathan F. and Rosenberg, Arnold L. and Knott, Judson D.},
	year         = 1987,
	publisher    = {University of Massachusetts},
	address      = {Amherst, MA, USA},
	source       = {http://www.ncstrl.org:8900/ncstrl/servlet/search?formname=detail\&id=oai\%3Ancstrlh\%3Aumass\_cs\%3Ancstrl.umassa\_cs\%2F\%2FUM-CS-1987-018}
}
% whole proceedings
@proceedings{Czerwinski:2008:1358628,
	title        = {CHI '08: CHI '08 extended abstracts on Human factors in computing systems},
	author       = {},
	year         = 2008,
	location     = {Florence, Italy},
	publisher    = {ACM},
	address      = {New York, NY, USA},
	isbn         = {978-1-60558-012-X},
	note         = {General Chair-Czerwinski, Mary and General Chair-Lund, Arnie and Program Chair-Tan, Desney},
	order_no     = 608085
}
% phdthesis
@phdthesis{Clarkson:1985:ACP:911891,
	title        = {Algorithms for Closest-Point Problems (Computational Geometry)},
	author       = {Clarkson, Kenneth Lee},
	year         = 1985,
	address      = {Stanford, CA, USA},
	note         = {AAT 8506171},
	advisor      = {Yao, Andrew C.},
	school       = {Stanford University}
}
% school is being picked up -- but not publisher (which is OK)
% Also -- the title is NOT being output in italics !!! Arrrrgh! - I fixed it. :-)
%%% compare with 'old'
%%% atsign-Phdthesis{Clarkson85,
%%%  author =       "Kenneth L. Clarkson",
%%%  year =         "1985",
%%%  title =        "Algorithms for Closest-Point Problems (Computational Geometry)",
%%%  school =       "Stanford University",
%%%  address =      "Palo Alto, CA",
%%%  note =         "UMI Order Number: AAT 8506171",
%%%  type =         "",
%%%  month =        "",
%%%}
% A bibliography
@article{1984:1040142,
	year         = 1984,
	journal      = {SIGCOMM Comput. Commun. Rev.},
	publisher    = {ACM},
	address      = {New York, NY, USA},
	volume       = {13-14},
	number       = {5-1},
	issn         = {0146-4833},
	key          = {{$\!\!$}},
	issue_date   = {January/April 1984}
}
% grinder
@inproceedings{2004:ITE:1009386.1010128,
	title        = {IEEE TCSC Executive Committee},
	year         = 2004,
	booktitle    = {Proceedings of the IEEE International Conference on Web Services},
	publisher    = {IEEE Computer Society},
	address      = {Washington, DC, USA},
	series       = {ICWS '04},
	pages        = {21--22},
	doi          = {http://dx.doi.org/10.1109/ICWS.2004.64},
	isbn         = {0-7695-2167-3},
	url          = {http://dx.doi.org/10.1109/ICWS.2004.64},
	key          = {IEEE},
	acmid        = 1010128
}
@book{Mullender:1993:DS:302430,
	title        = {Distributed systems (2nd Ed.)},
	year         = 1993,
	publisher    = {ACM Press/Addison-Wesley Publishing Co.},
	address      = {New York, NY, USA},
	isbn         = {0-201-62427-3},
	editor       = {Mullender, Sape}
}
% master thesis (as techreport and thesis)
@techreport{Petrie:1986:NAD:899644,
	title        = {New Algorithms for Dependency-Directed Backtracking (Master's thesis)},
	author       = {Petrie, Charles J.},
	year         = 1986,
	publisher    = {University of Texas at Austin},
	address      = {Austin, TX, USA},
	source       = {http://www.ncstrl.org:8900/ncstrl/servlet/search?formname=detail\&id=oai\%3Ancstrlh\%3Autexas\_cs\%3AUTEXAS\_CS\%2F\%2FAI86-33},
	school       = {University of Texas at Austin}
}
@book{book-minimal,
	title        = {Seminumerical Algorithms},
	author       = {Donald E. Knuth},
	year         = 1981,
	month        = {10~} # jan,
	publisher    = {Addison-Wesley},
	address      = {Reading, MA},
	series       = {The Art of Computer Programming},
	volume       = 2,
	edition      = {2nd}
}
% incollection (has an editor, title, and possibly a booktitle)
@incollection{KA:2001,
	title        = {The implementation of electronic commerce in SMEs in Singapore (as Incoll)},
	author       = {Kong, Wei-Chang},
	year         = 2001,
	booktitle    = {E-commerce and cultural values},
	publisher    = {IGI Publishing},
	address      = {Hershey, PA, USA},
	pages        = {51--74},
	isbn         = {1-59140-056-2},
	url          = {http://portal.acm.org/citation.cfm?id=887006.887010},
	numpages     = 24,
	acmid        = 887010
}
% with bibfield 'type' before chapter (note no editor)
@inbook{KAGM:2001,
	title        = {E-commerce and cultural values},
	author       = {Kong, Wei-Chang},
	year         = 2001,
	publisher    = {IGI Publishing},
	address      = {Hershey, PA, USA},
	pages        = {51--74},
	isbn         = {1-59140-056-2},
	url          = {http://portal.acm.org/citation.cfm?id=887006.887010},
	type         = {Name of Chapter:},
	chapter      = {The implementation of electronic commerce in SMEs in Singapore (Inbook-w-chap-w-type)},
	numpages     = 24,
	acmid        = 887010
}
%%% Notes! This is because the atsign-INBOOK citation type specifies EITHER
%%% editor or author, but not both. In my experiments with the harvard/dcu
%%% bibtex style (and presumably this applies to other styles too), bibtex
%%% ignores the editor information if author information exists in an
%%% atsign-INBOOK entry. atsign-INCOLLECTION is far more commonly used in my references,
%%% and in the absence of an editor I believe most bibtex styles will just
%%% ommit the editor from the reference - the chapter information will not
%%% end up in the in-text citation as you suggest it should be but at least
%%% there is a place to put the editor if necessary.
% was 'Inbook' -- changed to incollection - (editor is different to author) - need to tell Asad to codify as such.
@incollection{Kong:2002:IEC:887006.887010,
	title        = {Chapter 9},
	author       = {Kong, Wei-Chang},
	year         = 2002,
	month        = {},
	booktitle    = {E-commerce and cultural values (Incoll-w-text (chap 9) 'title')},
	publisher    = {IGI Publishing},
	address      = {Hershey, PA, USA},
	number       = {},
	pages        = {51--74},
	isbn         = {1-59140-056-2},
	url          = {http://portal.acm.org/citation.cfm?id=887006.887010},
	note         = {},
	editor       = {Theerasak Thanasankit},
	numpages     = 24,
	acmid        = 887010,
	type         = {}
}
% incol when the chapter is 'text' - due to presence of editor (different to author)
@incollection{Kong:2003:IEC:887006.887011,
	title        = {The implementation of electronic commerce in SMEs in Singapore (Incoll)},
	author       = {Kong, Wei-Chang},
	year         = 2003,
	booktitle    = {E-commerce and cultural values},
	publisher    = {IGI Publishing},
	address      = {Hershey, PA, USA},
	pages        = {51--74},
	isbn         = {1-59140-056-2},
	url          = {http://portal.acm.org/citation.cfm?id=887006.887010},
	editor       = {Thanasankit, Theerasak},
	numpages     = 24,
	acmid        = 887010
}
% ------ test
%incollection{Kong:2003:IEC:887006.887010,
% author = {Kong, Wei-Chang},
% chapter = {The implementation of electronic commerce in SMEs in Singapore (Incoll-text-in-chap)},
% booktitle = {booktitle E-commerce and cultural values},
% title =   {The title},
% editor = {Thanasankit, Theerasak},
% year = {2003},
% isbn = {1-59140-056-2},
% pages = {51--74},
% numpages = {24},
% url = {http://portal.acm.org/citation.cfm?id=887006.887010},
% acmid = {887010},
% publisher = {IGI Publishing},
% address = {Hershey, PA, USA},
%}
% ---------
% Need inbook with num in chapter
% and inbook with number in chapter
@inbook{Kong:2004:IEC:123456.887010,
	title        = {E-commerce and cultural values - (InBook-num-in-chap)},
	author       = {Kong, Wei-Chang},
	year         = 2004,
	month        = {},
	publisher    = {IGI Publishing},
	address      = {Hershey, PA, USA},
	number       = {},
	pages        = {51--74},
	isbn         = {1-59140-056-2},
	url          = {http://portal.acm.org/citation.cfm?id=887006.887010},
	note         = {},
	editor       = {Theerasak Thanasankit},
	chapter      = 9,
	numpages     = 24,
	acmid        = 887010,
	type         = {}
}
% and inbook with text in chapter
@inbook{Kong:2005:IEC:887006.887010,
	title        = {E-commerce and cultural values (Inbook-text-in-chap)},
	author       = {Kong, Wei-Chang},
	year         = 2005,
	month        = {},
	publisher    = {IGI Publishing},
	address      = {Hershey, PA, USA},
	number       = {},
	pages        = {51--74},
	isbn         = {1-59140-056-2},
	url          = {http://portal.acm.org/citation.cfm?id=887006.887010},
	note         = {},
	editor       = {Theerasak Thanasankit},
	chapter      = {The implementation of electronic commerce in SMEs in Singapore},
	type         = {Chapter:},
	numpages     = 24,
	acmid        = 887010
}
% and inbook with a num and type field
@inbook{Kong:2006:IEC:887006.887010,
	title        = {E-commerce and cultural values (Inbook-num chap)},
	author       = {Kong, Wei-Chang},
	year         = 2006,
	month        = {},
	publisher    = {IGI Publishing},
	address      = {Hershey, PA, USA},
	number       = {},
	pages        = {51--74},
	isbn         = {1-59140-056-2},
	url          = {http://portal.acm.org/citation.cfm?id=887006.887010},
	note         = {},
	editor       = {Theerasak Thanasankit},
	chapter      = 22,
	type         = {Chapter (in type field)},
	numpages     = 24,
	acmid        = 887010
}
% and incol coz we have a BLANK chapter - due to presence of editor
%atIncollection{Kong:2006:IEC:887006.887011,
%  author =     {Kong, Wei-Chang},
%  editor =     {Theerasak Thanasankit},
%  title =      "The title"
%  booktitle =  {E-commerce and cultural values (Incol-coz-blank-chap)},
%  year =       {2006},
%  address =    {Hershey, PA, USA},
%  publisher =  {IGI Publishing},
%  url =        {http://portal.acm.org/citation.cfm?id=887006.887010},
%  type =       {Type!},
%  chapter =    {},
%  pages =      {51--74},
%  numpages =   {24},
%  acmid =      {887010},
%  isbn =       {1-59140-056-2},
%  number =     "",
%  month =      "",
%  note =       "",
%}
@article{SaeediMEJ10,
	title        = {A library-based synthesis methodology for reversible logic},
	author       = {Mehdi Saeedi and Morteza Saheb Zamani and Mehdi Sedighi},
	year         = 2010,
	month        = apr,
	journal      = {Microelectron. J.},
	volume       = 41,
	number       = 4,
	pages        = {185--194}
}
@article{SaeediJETC10,
	title        = {Synthesis of Reversible Circuit Using Cycle-Based Approach},
	author       = {Mehdi Saeedi and Morteza Saheb Zamani and Mehdi Sedighi and Zahra Sasanian},
	year         = 2010,
	month        = dec,
	journal      = {J. Emerg. Technol. Comput. Syst.},
	volume       = 6,
	number       = 4
}
% Asad's new version
@article{Kirschmer:2010:AEI:1958016.1958018,
	title        = {Algorithmic Enumeration of Ideal Classes for Quaternion Orders},
	author       = {Kirschmer, Markus and Voight, John},
	year         = 2010,
	month        = jan,
	journal      = {SIAM J. Comput.},
	publisher    = {Society for Industrial and Applied Mathematics},
	address      = {Philadelphia, PA, USA},
	volume       = 39,
	number       = 5,
	pages        = {1714--1747},
	doi          = {https://doi.org/10.1137/080734467},
	issn         = {0097-5397},
	url          = {http://dx.doi.org/10.1137/080734467},
	issue_date   = {January 2010},
	numpages     = 34,
	acmid        = 1958018,
	keywords     = {ideal classes, maximal orders, number theory, quaternion algebras}
}
% incol due to presence of booktitle
@incollection{Hoare:1972:CIN:1243380.1243382,
	title        = {Chapter II: Notes on data structuring},
	author       = {Hoare, C. A. R.},
	year         = 1972,
	booktitle    = {Structured programming (incoll)},
	publisher    = {Academic Press Ltd.},
	address      = {London, UK, UK},
	pages        = {83--174},
	isbn         = {0-12-200550-3},
	url          = {http://portal.acm.org/citation.cfm?id=1243380.1243382},
	editor       = {Dahl, O. J. and Dijkstra, E. W. and Hoare, C. A. R.},
	numpages     = 92,
	acmid        = 1243382
}
% incol due to presence of booktitle
@incollection{Lee:1978:TQA:800025.1198348,
	title        = {Transcript of question and answer session},
	author       = {Lee, Jan},
	year         = 1981,
	booktitle    = {History of programming languages I (incoll)},
	publisher    = {ACM},
	address      = {New York, NY, USA},
	pages        = {68--71},
	doi          = {http://doi.acm.org/10.1145/800025.1198348},
	isbn         = {0-12-745040-8},
	url          = {http://doi.acm.org/10.1145/800025.1198348},
	editor       = {Wexelblat, Richard L.},
	numpages     = 4,
	acmid        = 1198348
}
% incol due to booktitle
@incollection{Dijkstra:1979:GSC:1241515.1241518,
	title        = {Go to statement considered harmful},
	author       = {Dijkstra, E.},
	year         = 1979,
	booktitle    = {Classics in software engineering (incoll)},
	publisher    = {Yourdon Press},
	address      = {Upper Saddle River, NJ, USA},
	pages        = {27--33},
	isbn         = {0-917072-14-6},
	url          = {http://portal.acm.org/citation.cfm?id=1241515.1241518},
	numpages     = 7,
	acmid        = 1241518
}
% incol due to booktitle
@incollection{Wenzel:1992:TVA:146022.146089,
	title        = {Three-dimensional virtual acoustic displays},
	author       = {Wenzel, Elizabeth M.},
	year         = 1992,
	booktitle    = {Multimedia interface design (incoll)},
	publisher    = {ACM},
	address      = {New York, NY, USA},
	pages        = {257--288},
	doi          = {10.1145/146022.146089},
	isbn         = {0-201-54981-6},
	url          = {http://portal.acm.org/citation.cfm?id=146022.146089},
	numpages     = 32,
	acmid        = 146089
}
% incol due to booktitle
@incollection{Mumford:1987:MES:54905.54911,
	title        = {Managerial expert systems and organizational change: some critical research issues},
	author       = {Mumford, E.},
	year         = 1987,
	booktitle    = {Critical issues in information systems research (incoll)},
	publisher    = {John Wiley \& Sons, Inc.},
	address      = {New York, NY, USA},
	pages        = {135--155},
	isbn         = {0-471-91281-6},
	url          = {http://portal.acm.org/citation.cfm?id=54905.54911},
	numpages     = 21,
	acmid        = 54911
}
@book{McCracken:1990:SSC:575315,
	title        = {Simplified Structured COBOL with Microsoft/MicroFocus COBOL},
	author       = {McCracken, Daniel D. and Golden, Donald G.},
	year         = 1990,
	publisher    = {John Wiley \& Sons, Inc.},
	address      = {New York, NY, USA},
	isbn         = {0471514071}
}
% Let's include Boris / BBeeton entries  (multi-volume works)
@book{MR781537,
	title        = {The analysis of linear partial differential operators. {III}},
	author       = {H{\"o}rmander, Lars},
	year         = 1985,
	publisher    = {Springer-Verlag},
	address      = {Berlin, Germany},
	series       = {Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences]},
	volume       = 275,
	pages        = {viii+525},
	isbn         = {3-540-13828-5},
	note         = {Pseudodifferential operators},
	mrclass      = {35-02 (35Sxx 47G05 58G15)},
	mrnumber     = {781536 (87d:35002a)},
	mrreviewer   = {Min You Qi}
}
@book{MR781536,
	title        = {The analysis of linear partial differential operators. {IV}},
	author       = {H{\"o}rmander, Lars},
	year         = 1985,
	publisher    = {Springer-Verlag},
	address      = {Berlin, Germany},
	series       = {Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences]},
	volume       = 275,
	pages        = {vii+352},
	isbn         = {3-540-13829-3},
	note         = {Fourier integral operators},
	mrclass      = {35-02 (35Sxx 47G05 58G15)},
	mrnumber     = {781537 (87d:35002b)},
	mrreviewer   = {Min You Qi}
}
%%%%%%%%%%%%%%%%%%%%%% Start of Aptara sample bib entries
% acmsmall-sam.bib
@inproceedings{Adya-01,
	title        = {A multi-radio unification protocol for {IEEE} 802.11 wireless networks},
	author       = {A. Adya and P. Bahl and J. Padhye and A.Wolman and L. Zhou},
	year         = 2004,
	booktitle    = {Proceedings of the IEEE 1st International Conference on Broadnets Networks (BroadNets'04)},
	publisher    = {IEEE},
	address      = {Los Alamitos, CA},
	pages        = {210--217}
}
@article{Akyildiz-01,
	title        = {Wireless Sensor Networks: A Survey},
	author       = {I. F. Akyildiz and W. Su and Y. Sankarasubramaniam and E. Cayirci},
	year         = 2002,
	journal      = {Comm. ACM},
	volume       = 38,
	number       = 4,
	pages        = {393--422}
}
@article{Akyildiz-02,
	title        = {A Survey on Wireless Multimedia Sensor Networks},
	author       = {I. F. Akyildiz and T. Melodia and K. R. Chowdhury},
	year         = 2007,
	journal      = {Computer Netw.},
	volume       = 51,
	number       = 4,
	pages        = {921--960}
}
@inproceedings{Bahl-02,
	title        = {{SSCH}: Slotted Seeded Channel Hopping for Capacity Improvement in {IEEE} 802.11 Ad-Hoc Wireless Networks},
	author       = {P. Bahl and R. Chancre and J. Dungeon},
	year         = 2004,
	booktitle    = {Proceeding of the 10th International Conference on Mobile Computing and Networking (MobiCom'04)},
	publisher    = {ACM},
	address      = {New York, NY},
	pages        = {112--117}
}
@misc{CROSSBOW,
	title        = {{XBOW} Sensor Motes Specifications},
	year         = 2008,
	note         = {http://www.xbow.com},
	key          = {CROSSBOW}
}
@article{Culler-01,
	title        = {Overview of Sensor Networks},
	author       = {D. Culler and D. Estrin and M. Srivastava},
	year         = 2004,
	journal      = {IEEE Comput.},
	publisher    = {IEEE},
	address      = {Los Alamitos, CA},
	volume       = 37,
	number       = {8 (Special Issue on Sensor Networks)},
	pages        = {41--49}
}
@misc{Harvard-01,
	title        = {{CodeBlue}: Sensor Networks for Medical Care},
	year         = 2008,
	note         = {http://www.eecs.harvard.edu/mdw/ proj/codeblue/},
	key          = {Harvard CodeBlue}
}
@inproceedings{Natarajan-01,
	title        = {Investigating Network Architectures for Body Sensor Networks},
	author       = {A. Natarajan and M. Motani and B. de Silva and K. Yap and K. C. Chua},
	year         = 2007,
	booktitle    = {Network Architectures},
	publisher    = {Keleuven Press},
	address      = {Dayton, OH},
	pages        = {322--328},
	editor       = {G. Whitcomb and P. Neece},
	eprint       = 960935712,
	primaryclass = {cs}
}
@techreport{Tzamaloukas-01,
	title        = {Channel-Hopping Multiple Access},
	author       = {A. Tzamaloukas and J. J. Garcia-Luna-Aceves},
	year         = 2000,
	address      = {Berkeley, CA},
	number       = {I-CA2301},
	institution  = {Department of Computer Science, University of California}
}
@book{Zhou-06,
	title        = {Body Sensor Networks},
	author       = {G. Zhou and J. Lu and C.-Y. Wan and M. D. Yarvis and J. A. Stankovic},
	year         = 2008,
	publisher    = {MIT Press},
	address      = {Cambridge, MA}
}
@mastersthesis{ko94,
	title        = {Mapping Powerlists onto Hypercubes},
	author       = {Jacob Kornerup},
	year         = 1994,
	note         = {(In preparation)},
	school       = {The University of Texas at Austin}
}
%month = "dec",}
@phdthesis{gerndt:89,
	title        = {Automatic Parallelization for Distributed-Memory Multiprocessing Systems},
	author       = {Michael Gerndt},
	year         = 1989,
	month        = dec,
	address      = {Bonn, Germany},
	school       = {University of Bonn}
}
@article{6:1:1,
	title        = {User recovery and reversal in interactive systems},
	author       = {J. E. {Archer, Jr.} and R. Conway and F. B. Schneider},
	year         = 1984,
	month        = jan,
	journal      = {ACM Trans. Program. Lang. Syst.},
	volume       = 6,
	number       = 1,
	pages        = {1--19}
}
@article{7:1:137,
	title        = {Generalizing specifications for uniformly implemented loops},
	author       = {D. D. Dunlop and V. R. Basili},
	year         = 1985,
	month        = jan,
	journal      = {ACM Trans. Program. Lang. Syst.},
	volume       = 7,
	number       = 1,
	pages        = {137--158}
}
@article{7:2:183,
	title        = {Towards monolingual programming environments},
	author       = {J. Heering and P. Klint},
	year         = 1985,
	month        = apr,
	journal      = {ACM Trans. Program. Lang. Syst.},
	volume       = 7,
	number       = 2,
	pages        = {183--213}
}
@book{knuth:texbook,
	title        = {The {\TeX{}book}},
	author       = {Donald E. Knuth},
	year         = 1984,
	publisher    = {Addison-Wesley},
	address      = {Reading, MA.}
}
@article{6:3:380,
	title        = {Distributed algorithms for finding centers and medians in networks},
	author       = {E. Korach and D.  Rotem and N. Santoro},
	year         = 1984,
	month        = jul,
	journal      = {ACM Trans. Program. Lang. Syst.},
	volume       = 6,
	number       = 3,
	pages        = {380--401}
}
@book{lamport:latex,
	title        = {\it {\LaTeX}: A Document Preparation System},
	author       = {Leslie Lamport},
	year         = 1986,
	publisher    = {Addison-Wesley},
	address      = {Reading, MA.}
}
@article{7:3:359,
	title        = {Program transformations in a denotational setting},
	author       = {F. Nielson},
	year         = 1985,
	month        = jul,
	journal      = {ACM Trans. Program. Lang. Syst.},
	volume       = 7,
	number       = 3,
	pages        = {359--379}
}
%testing
@inproceedings{reid:scribe,
	title        = {A high-level approach to computer document formatting},
	author       = {Brian K. Reid},
	year         = 1980,
	month        = jan,
	booktitle    = {Proceedings of the 7th Annual Symposium on Principles of Programming Languages},
	publisher    = {ACM},
	address      = {New York},
	pages        = {24--31}
}
@article{Zhou:2010:MMS:1721695.1721705,
	title        = {A multifrequency MAC specially designed for wireless sensor network applications},
	author       = {Zhou, Gang and Wu, Yafeng and Yan, Ting and He, Tian and Huang, Chengdu and Stankovic, John A. and Abdelzaher, Tarek F.},
	year         = 2010,
	month        = {April},
	journal      = {ACM Trans. Embed. Comput. Syst.},
	publisher    = {ACM},
	address      = {New York, NY, USA},
	volume       = 9,
	number       = 4,
	pages        = {39:1--39:41},
	doi          = {10.1145/1721695.1721705},
	issn         = {1539-9087},
	url          = {http://doi.acm.org/10.1145/1721695.1721705},
	issue_date   = {March 2010},
	articleno    = 39,
	numpages     = 41,
	acmid        = 1721705,
	keywords     = {Wireless sensor networks, media access control, multi-channel, radio interference, time synchronization}
}
@online{TUGInstmem,
	title        = {Institutional members of the {\TeX} Users Group},
	year         = 2017,
	url          = {http://wwtug.org/instmem.html},
	key          = {TUG},
	lastaccessed = {May 27, 2017}
}
@online{CTANacmart,
	title        = {acmart---{C}lass for typesetting publications of {ACM}},
	author       = {Boris Veytsman},
	url          = {http://www.ctan.org/pkg/acmart},
	lastaccessed = {May 27, 2017}
}
@article{bowman:reasoning,
	title        = {Reasoning About Naming Systems},
	author       = {Bowman, Mic and Debray, Saumya K. and Peterson, Larry L.},
	year         = 1993,
	month        = {November},
	journal      = {ACM Trans. Program. Lang. Syst.},
	volume       = 15,
	number       = 5,
	pages        = {795--825},
	doi          = {10.1145/161468.161471}
}
@article{braams:babel,
	title        = {Babel, a Multilingual Style-Option System for Use with LaTeX's Standard Document Styles},
	author       = {Braams, Johannes},
	year         = 1991,
	month        = {June},
	journal      = {TUGboat},
	volume       = 12,
	number       = 2,
	pages        = {291--301}
}
@inproceedings{clark:pct,
	title        = {Post Congress Tristesse},
	author       = {Malcolm Clark},
	year         = 1991,
	month        = {March},
	booktitle    = {TeX90 Conference Proceedings},
	pages        = {84--89},
	organization = {TeX Users Group}
}
@article{herlihy:methodology,
	title        = {A Methodology for Implementing Highly Concurrent Data Objects},
	author       = {Herlihy, Maurice},
	year         = 1993,
	month        = {November},
	journal      = {ACM Trans. Program. Lang. Syst.},
	volume       = 15,
	number       = 5,
	pages        = {745--770},
	doi          = {10.1145/161468.161469}
}
@book{salas:calculus,
	title        = {Calculus: One and Several Variable},
	author       = {S.L. Salas and Einar Hille},
	year         = 1978,
	publisher    = {John Wiley and Sons},
	address      = {New York}
}
@manual{Fear05,
	title        = {Publication quality tables in {\LaTeX}},
	author       = {Simon Fear},
	year         = 2005,
	month        = {April},
	note         = {\url{http://www.ctan.org/pkg/booktabs}}
}
@misc{TeXFAQ,
	title        = {{UK} List of {\TeX} Frequently Asked Questions},
	author       = {{UK \TeX{} Users Group}},
	year         = 2016,
	howpublished = {\url{http://www.tex.ac.uk}}
}
@manual{Downes04:amsart,
	title        = {The \textsf{amsart}, \textsf{amsproc}, and \textsf{amsbook} document~classes},
	author       = {Michael Downes and Barbara Beeton},
	year         = 2004,
	month        = aug,
	note         = {\url{http://www.ctan.org/pkg/amslatex}},
	organization = {American Mathematical Society}
}
@manual{Fiorio15,
	title        = {{a}lgorithm2e.sty---package for algorithms},
	author       = {Cristophe Fiorio},
	year         = 2015,
	month        = oct,
	note         = {\url{http://www.ctan.org/pkg/algorithm2e}}
}
@manual{Brito09,
	title        = {The algorithms bundle},
	author       = {Rogerio Brito},
	year         = 2009,
	month        = aug,
	note         = {\url{http://www.ctan.org/pkg/algorithms}}
}
@manual{Heinz15,
	title        = {The Listings Package},
	author       = {Carsten Heinz and Brooks Moses and Jobst Hoffmann},
	year         = 2015,
	month        = jun,
	note         = {\url{http://www.ctan.org/pkg/listings}}
}
@manual{ACMIdentityStandards,
	title        = {{ACM} Visual Identity Standards},
	year         = 2007,
	note         = {\url{http://identitystandards.acm.org}},
	organization = {Association for Computing Machinery}
}
@manual{Sommerfeldt13:Subcaption,
	title        = {The subcaption package},
	author       = {Axel Sommerfeldt},
	year         = 2013,
	month        = apr,
	note         = {\url{http://www.ctan.org/pkg/subcaption}}
}
@manual{Nomencl,
	title        = {A package to create a nomenclature},
	author       = {Boris Veytsman and Bern Schandl and Lee Netherton and C. V. Radhakrishnan},
	year         = 2005,
	month        = sep,
	note         = {\url{http://www.ctan.org/pkg/nomencl}}
}
@manual{Talbot16:Glossaries,
	title        = {User Manual for glossaries.sty v4.25},
	author       = {Nicola L. C. Talbot},
	year         = 2016,
	month        = jun,
	note         = {\url{http://www.ctan.org/pkg/subcaption}}
}
@book{Mullender:1993:DS:302430,
	title        = {Distributed systems (2nd Ed.)},
	year         = 1993,
	publisher    = {ACM Press/Addison-Wesley Publishing Co.},
	address      = {New York, NY, USA},
	isbn         = {0-201-62427-3},
	editor       = {Mullender, Sape}
}
% master thesis (as techreport and thesis)
@article{Wells:Mosbergen:Korn:2013,
	title        = {Stemformatics: Visualisation and sharing of stem cell gene expression},
	author       = {Christine A. Wells and Rowland Mosbergen and Othmar Korn and Jarny Choi and Nick Seidenman and Nicholas A. Matigian and Alejandra M. Vitale and Jill Shepherd},
	year         = 2013,
	journal      = {Stem Cell Research},
	volume       = 10,
	number       = 3,
	pages        = {387--395}
}
@article{Choi:Pacheco:2018,
	title        = {Stemformatics: visualize and download curated stem cell data},
	author       = {Choi, Jarny and Pacheco, Chris M and Mosbergen, Rowland and Korn, Othmar and Chen, Tyrone and Nagpal, Isha and Englart, Steve and Angel, Paul W and Wells, Christine A},
	year         = 2019,
	journal      = {Nucleic Acids Research},
	volume       = 47,
	number       = {D1},
	pages        = {D841--D846}
}
@article{Zerbino:Achuthan:2018,
	title        = {Ensembl 2018},
	author       = {Zerbino, Daniel R and Achuthan, Premanand and Akanni, Wasiu and Amode, M~Ridwan and Barrell, Daniel and Bhai, Jyothish and Billis, Konstantinos and Cummins, Carla and Gall, Astrid and Giron, Carlos Garcia and Gil, Laurent and Gordon, Leo and Haggerty, Leanne and Haskell, Erin and Hourlier, Thibaut and Izuogu, Osagie G and Janacek, Sophie H and Juettemann, Thomas and To, Jimmy Kiang and Laird, Matthew R and Lavidas, Ilias and Liu, Zhicheng and Loveland, Jane E and Maurel, Thomas and McLaren, William and Moore, Benjamin and Mudge, Jonathan and Murphy, Daniel N and Newman, Victoria and Nuhn, Michael and Ogeh, Denye and Ong, Chuang Kee and Parker, Anne and Patricio, Mateus and Riat, Harpreet Singh and Schuilenburg, Helen and Sheppard, Dan and Sparrow, Helen and Taylor, Kieron and Thormann, Anja and Vullo, Alessandro and Walts, Brandon and Zadissa, Amonida and Frankish, Adam and Hunt, Sarah E and Kostadima, Myrto and Langridge, Nicholas and Martin, Fergal J and Muffato, Matthieu and Perry, Emily and Ruffier, Magali and Staines, Dan M and Trevanion, Stephen J and Aken, Bronwen L and Cunningham, Fiona and Yates, Andrew and Flicek, Paul},
	year         = 2018,
	journal      = {Nucleic Acids Research},
	volume       = 46,
	number       = {D1},
	pages        = {D754--D761}
}
@article{OLeary:Wright:Brister:2016,
	title        = {Reference sequence ({R}ef{S}eq) database at {NCBI}: current status, taxonomic expansion, and functional annotation},
	author       = {O'Leary, Nuala A. and Wright, Mathew W. and Brister, J. Rodney and Ciufo, Stacy and Haddad, Diana and McVeigh, Rich and Rajput, Bhanu and Robbertse, Barbara and Smith-White, Brian and Ako-Adjei, Danso and Astashyn, Alexander and Badretdin, Azat and Bao, Yiming and Blinkova, Olga and Brover, Vyacheslav and Chetvernin, Vyacheslav and Choi, Jinna and Cox, Eric and Ermolaeva, Olga and Farrell, Catherine M. and Goldfarb, Tamara and Gupta, Tripti and Haft, Daniel and Hatcher, Eneida and Hlavina, Wratko and Joardar, Vinita S. and Kodali, Vamsi K. and Li, Wenjun and Maglott, Donna and Masterson, Patrick and McGarvey, Kelly M. and Murphy, Michael R. and O'Neill, Kathleen and Pujar, Shashikant and Rangwala, Sanjida H. and Rausch, Daniel and Riddick, Lillian D. and Schoch, Conrad and Shkeda, Andrei and Storz, Susan S. and Sun, Hanzhen and Thibaud-Nissen, Francoise and Tolstoy, Igor and Tully, Raymond E. and Vatsan, Anjana R. and Wallin, Craig and Webb, David and Wu, Wendy and Landrum, Melissa J. and Kimchi, Avi and Tatusova, Tatiana and DiCuccio, Michael and Kitts, Paul and Murphy, Terence D. and Pruitt, Kim D.},
	year         = 2016,
	journal      = {Nucleic Acids Research},
	volume       = 44,
	number       = {D1},
	pages        = {D733--D745}
}
@inproceedings{Roberts:Fushman:Voorhees:2017,
	title        = {Overview of the {TREC} 2017 Precision Medicine Track},
	author       = {K. Roberts and D. Demner-Fushman and E. Voorhees and W. Hersh and Steven Bedrick and A. Lazar and S. Pant},
	year         = 2017,
	booktitle    = {TREC},
	address      = {Gaithersburg, MD},
}
@article{Kharazmi:Scholer:2016,
	title        = {Examining Additivity and Weak Baselines},
	author       = {Kharazmi, Sadegh and Scholer, Falk and Vallet, David and Sanderson, Mark},
	year         = 2016,
	journal      = {ACM Trans. Inf. Syst.},
	volume       = 34,
	number       = 4,
	pages        = {23:1--23:18},
	articleno    = 23,
	keywords     = {Baselines, evaluation, information retrieval}
}
@inproceedings{Mohan:Fiorini:Lu:2017,
	title        = {Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs},
	author       = {Mohan, Sunil and Fiorini, Nicolas and Kim, Sun and Lu, Zhiyong},
	year         = 2017,
	booktitle    = {BioNLP},
	location     = {Vancouver, Canada},
	pages        = {222--231}
}
@misc{Nguyen:Karimi:2018,
	title        = {Benchmarking Clinical Decision Support Search},
	author       = {V. Nguyen and S. Karimi and S. Falamaki and C. Paris},
	year         = 2018,
	url          = {https://arxiv.org/abs/1801.09322}
}
@inproceedings{Koopman:Zuccon:Russel:2017,
	title        = {A Task-oriented Search Engine for Evidence-based Medicine},
	author       = {Koopman, B. and Zuccon, G. and Russell, J.},
	year         = 2017,
	booktitle    = {SIGIR},
	address      = {Shinjuku, Tokyo, Japan},
	pages        = {1329--1332},
	keywords     = {evidence-based medicine, medical information retrieval, search engines}
}
@article{Lu:2011,
	title        = {Pub{M}ed and beyond: a survey of web tools for searching biomedical literature},
	author       = {Z. Lu},
	year         = 2011,
	journal      = {The Journal of Biological Databases and Curation},
	volume       = {baq036}
}
@inproceedings{Gkotsis:Velupillai:2016,
	title        = {Don't Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records},
	author       = {Gkotsis, G. and Velupillai, S. and Oellrich, A. and Dean, H. and Liakata, M. and Dutta, R.},
	year         = 2016,
	booktitle    = {CLPsych at NAACL},
	address      = {San Diego, CA},
	pages        = {95--105}
}
@article{Chapman:Bridwell:2001,
	title        = {A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries},
	author       = {W. Chapman and W. Bridewell and P. Hanbury and G. Coopera and B. Buchanan},
	year         = 2001,
	journal      = {J Biomed Inform.},
	volume       = 34,
	number       = 5,
	pages        = {301--310}
}
@inproceedings{Marshall:Kuiper:2017,
	title        = {Automating Biomedical Evidence Synthesis: RobotReviewer},
	author       = {Marshall, Iain and Kuiper, Jo{\"e}l and Banner, Edward and Wallace, Byron},
	year         = 2017,
	booktitle    = {Proceedings of ACL 2017, System Demonstrations},
	address      = {Vancouver, Canada},
	pages        = {7--12}
}
@article{Kiritchenko:Carini:2010,
	title        = {Exa{CT}: automatic extraction of clinical trial characteristics from journal publications},
	author       = {Kiritchenko, S and de Bruijn, B and Carini, S and Martin, J and Sim, I},
	year         = 2010,
	journal      = {BMC Medical Informatics and Decision Making},
	volume       = 10,
	number       = 56
}
@inproceedings{Lucchese:Muntean:2017,
	title        = {Rank{E}val: An Evaluation and Analysis Framework for Learning-to-Rank Solutions},
	author       = {Lucchese, C. and Muntean, C. and Nardini, F. and Perego, R. and Trani, S.},
	year         = 2017,
	booktitle    = {The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval},
	address      = {Shinjuku, Tokyo, Japan},
	pages        = {1281--1284},
	keywords     = {analysis, evaluation, learning to rank}
}
@book{HerSH:2015,
	title        = {Information Retrieval for Healthcare},
	author       = {W. Hersh},
	year         = 2015,
	pages        = {467--505}
}
@inproceedings{Jimmy:Zuccon:Koopman:2018,
	title        = {Choices in Knowledge-Base Retrieval for Consumer Health Search},
	author       = {Jimmy and Zuccon, G. and Koopman, B.},
	year         = 2018,
	booktitle    = {ECIR},
	address      = {Grenoble, France}
}
@inproceedings{CatGarfield:2018,
	title        = {Team {C}at-{G}arfield at {TREC} 2018 Precision Medicine Track},
	author       = {X. Zhou and X. Chen and J. Song and G. Zhao and J. Wu},
	year         = 2018,
	booktitle    = {TREC},
	address      = {Gaithersburg, MD}
}
@inproceedings{UDGUBioTM:2017,
	title        = {{UD{\_}GU{\_}BioTM} at {TREC} 2017: Precision Medicine Track},
	author       = {A. Ashique Mahmood and G. Li and S. Rao and P. McGarvey and C. Wu and S. Madhavan and K. Vijay{-}Shanker},
	year         = 2017,
	booktitle    = {TREC},
	address      = {Gaithersburg, MD}
}
@inproceedings{Goodwin:Skinner:2017,
	title        = {{UTD HLTRI} at {TREC} 2017: Precision Medicine Track},
	author       = {T. Goodwin and M. Skinner and S. Harabagiu},
	year         = 2017,
	booktitle    = {TREC},
	address      = {Gaithersburg, MD}
}
@article{LETOR:2010,
	title        = {{LETOR}: A Benchmark Collection for Research on Learning to Rank for Information Retrieval},
	author       = {Qin, T. and Liu, T-Y and Xu, J. and Li, H.},
	year         = 2010,
	journal      = {Information Retrieval},
	volume       = 13,
	number       = 4,
	pages        = {346--374},
	keywords     = {Benchmark datasets, Feature extraction, Information retrieval, Learning to rank}
}
@inproceedings{MCRank:2008,
	title        = {Mc{R}ank: Learning to Rank Using Multiple Classification and Gradient Boosting},
	author       = {P. Li and C. Burges and Q. Wu},
	year         = 2008,
	booktitle    = {NIPS},
	location     = {Vancouver, Canada},
	pages        = {897--904}
}
@inproceedings{Transformer:2017,
	title        = {Attention is All you Need},
	author       = {A. Vaswani and N. Shazeer and N. Parmar and J. Uszkoreit and L. Jones and A. Gomez and L. Kaiser and I. Polosukhin},
	year         = 2017,
	booktitle    = {NIPS},
	location     = {Long Beach, CA},
	pages        = {6000--6010},
    url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf}
}
@article{BERT:2018,
	title        = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding},
	author       = {J. Devlin and M. Chang and K. Lee and K. Toutanova},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1810.04805},
	eprint       = {1810.04805}
}
@article{Wright:Maloney:2015,
	title        = {Assessing information system readiness for mitigating malpractice risk through simulation: results of a multi-site study},
	author       = {Wright, A. and Maloney, F. and Wien, M. and Samal, L. and Emani, S. and Zuccotti, G.},
	year         = 2015,
	journal      = {Journal of the American Medical Informatics Association},
	volume       = 22,
	number       = 5,
	pages        = {1020--1028}
}
@article{Roberts:Boland:pruinelli:2016,
	title        = {Biomedical informatics advancing the national health agenda: The {AMIA} 2015 year-in-review in clinical and consumer informatics},
	author       = {Roberts, K. and Boland, M. and Pruinelli, L. and Dcruz, J. and Berry, A. and Georgsson, M. and Hazen, R. and Sarmiento, R. and Backonja, U. and Yu, K. and Jiang, Y. and Brennan, P.},
	year         = 2016,
	journal      = {Journal of the American Medical Informatics Association},
	volume       = 24,
	number       = {e1},
	pages        = {e185--e190}
}
@inproceedings{Ling:Hasan:Filannino:2017,
	title        = {A Hybrid Approach to Precision Medicine-related Biomedical Article Retrieval and Clinical Trial Matching},
	author       = {Y. Ling and S.A. Hasan and M. Filannino and K.P. Buchan and K. Lee and J. Liu and W. Boag and D. Jin and O. Uzuner and K. Lee and  V. Datla and A. Qadir and O. Farri},
	year         = 2017,
	address      = {Gaithersburg, MD},
	conference   = {TREC}
}
@article{maeda2005role,
	title        = {Role of the proto-oncogene Pokemon in cellular transformation and ARF repression},
	author       = {Maeda, T. and Hobbs, R. and Merghoub, T. and Guernah, I. and Zelent, A. and Cordon-Cardo, C. and Teruya-Feldstein, J. and Pandolfi, P.},
	year         = 2005,
	journal      = {Nature},
	volume       = 433,
	number       = 7023,
	pages        = 278
}
@misc{simonite2005pokemon,
	title        = {Pokemon blocks gene name},
	author       = {Simonite, Tom},
	year         = 2005,
	publisher    = {Nature Publishing Group}
}
@article{Schardt:Adams:2007,
	title        = {Utilization of the {PICO} framework to improve searching {P}ub{M}ed for clinical questions},
	author       = {Schardt, C. and Adams, M. and Owens, T. and Keitz, S. and Fontelo, P.},
	year         = 2007,
	journal      = {BMC Med Inform Decis Mak.},
	volume       = 7,
	number       = 16
}
@article{Smith:2018,
	title        = {A disciplined approach to neural network hyper-parameters: Part 1 - learning rate, batch size, momentum, and weight decay},
	author       = {L. Smith},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1803.09820},
	url          = {http://arxiv.org/abs/1803.09820},
	archiveprefix = {arXiv},
	eprint       = {1803.09820},
	timestamp    = {Mon, 13 Aug 2018 16:46:45 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1803-09820},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{NovaLab:2018,
	title        = {Learning to rank clinical trials with rule-based criteria},
	author       = {G. Araujo and A. Mourao and J. Magalhaes},
	year         = 2018,
	booktitle    = {TREC}
}
@inproceedings{SIB:2018,
	title        = {SIB Text Mining at TREC 2018 Precision Medicine Track},
	author       = {E. Pasche, P. van Rijen, J. Gobeill, A. Mottaz, L. Motin, P. Ruch},
	year         = 2018,
	booktitle    = {TREC}
}
@inproceedings{MayoNLP:2018,
	title        = {MayoNLPTeam at the TREC 2018 Precision Medicine Track: Simple Information Retrieval Approach Is the Best},
	author       = {Y. Wang, A. Wen, S. Liu, H. Liu},
	year         = 2018,
	booktitle    = {TREC}
}
@inproceedings{exbert,
	title        = {ex{BERT}: Extending Pre-trained Models with Domain-specific Vocabulary Under Constrained Training Resources},
	author       = {Tai, Wen  and Kung, H. T.  and Dong, Xin  and Comiter, Marcus  and Kuo, Chang-Fu},
	year         = 2020,
	month        = nov,
	booktitle    = {Findings of the Association for Computational Linguistics: EMNLP 2020},
	publisher    = {Association for Computational Linguistics},
	address      = {Online},
	pages        = {1433--1439},
	doi          = {10.18653/v1/2020.findings-emnlp.129},
	url          = {https://www.aclweb.org/anthology/2020.findings-emnlp.129},
	abstract     = {We introduce exBERT, a training method to extend BERT pre-trained models from a general domain to a new pre-trained model for a specific domain with a new additive vocabulary under constrained training resources (i.e., constrained computation and data). exBERT uses a small extension module to learn to adapt an augmenting embedding for the new domain in the context of the original BERT{'}s embedding of a general vocabulary. The exBERT training method is novel in learning the new vocabulary and the extension module while keeping the weights of the original BERT model fixed, resulting in a substantial reduction in required training resources. We pre-train exBERT with biomedical articles from ClinicalKey and PubMed Central, and study its performance on biomedical downstream benchmark tasks using the MTL-Bioinformatics-2016 datasets. We demonstrate that exBERT consistently outperforms prior approaches when using limited corpus and pre-training computation resources.}
}
@inproceedings{vocabulary_expansion,
	title        = {Improving Pre-Trained Multilingual Model with Vocabulary Expansion},
	author       = {Wang, Hai  and Yu, Dian  and Sun, Kai  and Chen, Jianshu  and Yu, Dong},
	year         = 2019,
	month        = nov,
	booktitle    = {Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)},
	publisher    = {Association for Computational Linguistics},
	address      = {Hong Kong, China},
	pages        = {316--327},
	doi          = {10.18653/v1/K19-1030},
	url          = {https://www.aclweb.org/anthology/K19-1030},
	abstract     = {Recently, pre-trained language models have achieved remarkable success in a broad range of natural language processing tasks. However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language model over large-scale corpora for each language. Instead of exhaustively pre-training monolingual language models independently, an alternative solution is to pre-train a powerful multilingual deep language model over large-scale corpora in hundreds of languages. However, the vocabulary size for each language in such a model is relatively small, especially for low-resource languages. This limitation inevitably hinders the performance of these multilingual models on tasks such as sequence labeling, wherein in-depth token-level or sentence-level understanding is essential. In this paper, inspired by previous methods designed for monolingual settings, we investigate two approaches (i.e., joint mapping and mixture mapping) based on a pre-trained multilingual model BERT for addressing the out-of-vocabulary (OOV) problem on a variety of tasks, including part-of-speech tagging, named entity recognition, machine translation quality estimation, and machine reading comprehension. Experimental results show that using mixture mapping is more promising. To the best of our knowledge, this is the first work that attempts to address and discuss the OOV issue in multilingual settings.}
}
@inproceedings{nguyen-etal-2019-investigating,
	title        = {Investigating the Effect of Lexical Segmentation in Transformer-based Models on Medical Datasets},
	author       = {Nguyen, Vincent  and Karimi, Sarvnaz  and Xing, Zhenchang},
	year         = 2019,
	month        = {4--6 } # dec,
	booktitle    = {Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association},
	publisher    = {Australasian Language Technology Association},
	address      = {Sydney, Australia},
	pages        = {165--171},
	url          = {https://www.aclweb.org/anthology/U19-1022},
	abstract     = {}
}
@article{zipf_vocab,
	title        = {Estimating Vocabulary Size},
	author       = {Alvar EllegaRd},
	year         = 1960,
	volume       = 16,
	pages        = {219--244},
	url          = {https://doi.org/10.1080/00437956.1960.11659728},
	issue        = 2
}
@article{JaccardIndex,
	title        = {THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1},
	author       = {Paul Jaccard},
	year         = 1912,
	journal      = {New Phytologist},
	volume       = 11,
	pages        = {37--50},
	url          = {https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/j.1469-8137.1912.tb05611.x}
}
@inproceedings{biomedical_larger_nvidia,
	title        = {{B}io{M}egatron: Larger Biomedical Domain Language Model},
	author       = {Shin, Hoo-Chang  and Zhang, Yang  and Bakhturina, Evelina  and Puri, Raul  and Patwary, Mostofa  and Shoeybi, Mohammad  and Mani, Raghav},
	year         = 2020,
	month        = nov,
	booktitle    = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
	address      = {Online},
	pages        = {4700--4706},
	doi          = {10.18653/v1/2020.emnlp-main.379},
	url          = {https://www.aclweb.org/anthology/2020.emnlp-main.379},
	abstract     = {There has been an influx of biomedical domain-specific language models, showing language models pre-trained on biomedical text perform better on biomedical domain benchmarks than those trained on general domain text corpora such as Wikipedia and Books. Yet, most works do not study the factors affecting each domain language application deeply. Additionally, the study of model size on domain-specific models has been mostly missing. We empirically study and evaluate several factors that can affect performance on domain language applications, such as the sub-word vocabulary set, model size, pre-training corpus, and domain transfer. We show consistent improvements on benchmarks with our larger BioMegatron model trained on a larger domain corpus, contributing to our understanding of domain language model applications. We demonstrate noticeable improvements over the previous state-of-the-art (SOTA) on standard biomedical NLP benchmarks of question answering, named entity recognition, and relation extraction. Code and checkpoints to reproduce our experiments are available at [github.com/NVIDIA/NeMo].}
}
@misc{Wikiextractor2015,
	title        = {WikiExtractor},
	author       = {Giusepppe Attardi},
	year         = 2015,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/attardi/wikiextractor}}
}
@inproceedings{bpe,
	title        = {Neural Machine Translation of Rare Words with Subword Units},
	author       = {Sennrich, Rico  and Haddow, Barry  and Birch, Alexandra},
	year         = 2016,
	month        = aug,
	booktitle    = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
	address      = {Berlin, Germany},
	pages        = {1715--1725},
	doi          = {10.18653/v1/P16-1162},
	url          = {https://www.aclweb.org/anthology/P16-1162}
}
@mastersthesis{vocabulary_insertion_thesis_legal,
	title        = {Effects of Inserting Domain Vocabulary and Fine-tuning BERT for German Legal Language},
	author       = {Chin Man Yeung Tai},
	year         = 2019,
	address      = {Netherlands},
	url          = {https://essay.utwente.nl/80128/},
	school       = {University of Twente}
}
@article{hoc_dataset,
	title        = {{Automatic semantic classification of scientific literature according to the hallmarks of cancer}},
	author       = {Baker, Simon and Silins, Ilona and Guo, Yufan and Ali, Imran and Hogberg, Johan and Stenius, Ulla and Korhonen, Anna},
	year         = 2015,
	month        = 10,
	journal      = {Bioinformatics},
	volume       = 32,
	number       = 3,
	pages        = {432--440},
	doi          = {10.1093/bioinformatics/btv585},
	issn         = {1367-4803},
	url          = {https://doi.org/10.1093/bioinformatics/btv585},
	eprint       = {https://academic.oup.com/bioinformatics/article-pdf/32/3/432/19568147/btv585.pdf}
}
@inproceedings{chemprot_dataset,
	title        = {Overview of the BioCreative VI chemical-protein interaction Track},
	author       = {Martin Krallinger and O. Rabal and S. A. Akhondi and M. Perez and J. Santamaria and Gael Perez Rodriguez and G. Tsatsaronis and Ander Intxaurrondo and J. A. Lopez and Umesh Nandal and E. V. Buel and A. Chandrasekhar and Marleen Rodenburg and A.G L{\ae}greid and Marius A. Doornenbal and J. Oyarzabal and A. Lourencco and A. Valencia},
	year         = 2017,
	booktitle    = {Proceedings of BioCreative},
	pages        = {141--146},
	url          = {https://biocreative.bioinformatics.udel.edu/media/store/files/2017/chemprot\_overview\_v03.pdf}
}
@article{ddi_dataset,
	title        = {The DDI corpus: An annotated corpus with pharmacological substances and drug–drug interactions},
	author       = {Maria Herrero-Zazo and Isabel Segura-Bedmar and Paloma Martinez and Thierry Declerck},
	year         = 2013,
	journal      = {Journal of Biomedical Informatics},
	volume       = 46,
	number       = 5,
	pages        = {914--920},
	doi          = {https://doi.org/10.1016/j.jbi.2013.07.011},
	issn         = {1532-0464},
	url          = {http://www.sciencedirect.com/science/article/pii/S1532046413001123}
}
@book{zipf_law,
	title        = {The Psychobiology of Language},
	author       = {George Zipf},
	year         = 1936,
	address      = {London, Routledge},
	url          = {https://mitpress.mit.edu/books/psycho-biology-language}
}
@inproceedings{domain-specific-polysemy,
	title        = {Determining Domain-Specific Differences of Polysemous Words Using Context Information},
	author       = {Daniel Toews and Leif Van Holland},
	year         = 2019,
	booktitle    = {Proceedings of the 25th International Working Conference on Requirement Engineering: Foundation for Software Quality},
	url          = {http://ceur-ws.org/Vol-2376/NLP4RE19\_paper02.pdf}
}
@book{jurafsky2009speech,
	title        = {Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition},
	author       = {Jurafsky, D. and Martin, J.H.},
	year         = 2009,
	publisher    = {Pearson Prentice Hall},
	series       = {Prentice Hall series in artificial intelligence},
	isbn         = 9780131873216,
	url          = {https://books.google.com.au/books?id=fZmj5UNK8AQC},
	lccn         = 2008010335
}
@inproceedings{hamilton-etal-2016-diachronic-polysemy,
	title        = {Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change},
	author       = {Hamilton, William L.  and Leskovec, Jure  and Jurafsky, Dan},
	year         = 2016,
	month        = aug,
	booktitle    = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics},
	address      = {Berlin, Germany},
	pages        = {1489--1501},
	doi          = {10.18653/v1/P16-1141},
	url          = {https://www.aclweb.org/anthology/P16-1141}
}
@inproceedings{oversegmentation-2020,
	title        = {An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels},
	author       = {Chalkidis, Ilias  and Fergadiotis, Manos  and Kotitsas, Sotiris  and Malakasiotis, Prodromos  and Aletras, Nikolaos  and Androutsopoulos, Ion},
	year         = 2020,
	month        = nov,
	booktitle    = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
	address      = {Online},
	pages        = {7503--7515},
	doi          = {10.18653/v1/2020.emnlp-main.607},
	url          = {https://www.aclweb.org/anthology/2020.emnlp-main.607}
}
@article{huggingface_paper,
	title        = {HuggingFace's Transformers: State-of-the-art Natural Language Processing},
	author       = {Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Remi Louf and Morgan Funtowicz and Jamie Brew},
	year         = 2019,
	journal      = {CoRR},
	url          = {http://arxiv.org/abs/1910.03771},
	eprint       = {1910.03771}
}
@incollection{castrophic_forgetting,
	title        = {Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem},
	author       = {Michael McCloskey and Neal J. Cohen},
	year         = 1989,
	publisher    = {Academic Press},
	series       = {Psychology of Learning and Motivation},
	volume       = 24,
	pages        = {109--165},
	doi          = {https://doi.org/10.1016/S0079-7421(08)60536-8},
	issn         = {0079-7421},
	url          = {https://www.sciencedirect.com/science/article/pii/S0079742108605368},
	editor       = {Gordon H. Bower}
}
@article{deep-multi-task-learning,
	title        = {Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding},
	author       = {Liu, Xiaodong and He, Pengcheng and Chen, Weizhu and Gao, Jianfeng},
	year         = 2019,
	journal      = {CoRR},
	volume       = {arXiv:1904.0948},
	url          = {http://arxiv.org/abs/1904.0948}
}
@article{decanlp,
	title        = {The Natural Language Decathlon: Multitask Learning as Question Answering},
	author       = {Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1806.08730},
	url          = {http://arxiv.org/abs/1806.08730},
	archiveprefix = {arXiv},
	eprint       = {1806.08730},
	timestamp    = {Mon, 13 Aug 2018 16:49:05 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/abs-1806-08730},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{neural_architecture_search,
	title        = {Neural Architecture Search with Reinforcement Learning},
	author       = {Barret Zoph and Quoc Le},
	year         = 2016,
	journal      = {CoRR},
	volume       = {abs/1611.01578},
	url          = {http://arxiv.org/abs/1611.01578},
	archiveprefix = {arXiv},
	eprint       = {1611.01578},
	timestamp    = {Mon, 13 Aug 2018 16:46:24 +0200},
	biburl       = {https://dblp.org/rec/bib/journals/corr/ZophL16},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{Karimi:Nguyen:2018,
	title        = {{A2A:} Benchmark Your Clinical Decision Support Search},
	author       = {S. Karimi and V. Nguyen and F. Scholer and B. Jin and S. Falamaki},
	year         = 2018,
	booktitle    = {The 41st International {ACM} {SIGIR} Conference on Research {\&} Development in Information Retrieval},
	address      = {Ann Arbor, MI},
	pages        = {1277--1280}
}
@inproceedings{Scells:2018,
	title        = {An Information Retrieval Experiment Framework for Domain Specific Applications},
	author       = {Scells, H. and Locke, D. and Zuccon, G.},
	year         = 2018,
	booktitle    = {SIGIR},
	address      = {Ann Arbor, MI},
	pages        = {1281--1284},
	keywords     = {experimental framework, information retrieval, pipeline}
}
@inproceedings{UNTIIA:2018,
	title        = {UNT Precision Medicine Information Retrieval at TREC 2018},
	author       = {H. Chen, J. Ding, J. Chen, A. Cleveland, X. Tang},
	year         = 2018,
	booktitle    = {TREC}
}
@article{metamaplite,
	title        = {{MetaMap {L}ite: an evaluation of a new Java implementation of MetaMap}},
	author       = {Aronson, A. and Rogers, W. and Demner-Fushman, D.},
	year         = 2017,
	journal      = {Journal of the American Medical Informatics Association},
	volume       = 24,
	number       = 4,
	pages        = {841--844}
}
@inproceedings{poerner-etal-2020-inexpensive,
	title        = {Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical {NER} and Covid-19 {QA}},
	author       = {Poerner, Nina  and Waltinger, Ulli  and Schutze, Hinrich},
	year         = 2020,
	month        = nov,
	booktitle    = {Findings of the Association for Computational Linguistics: EMNLP 2020},
	publisher    = {Association for Computational Linguistics},
	address      = {Online},
	pages        = {1482--1490},
	doi          = {10.18653/v1/2020.findings-emnlp.134},
	url          = {https://aclanthology.org/2020.findings-emnlp.134},
	abstract     = {Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by unsupervised pretraining on target-domain text. While successful, this approach is expensive in terms of hardware, runtime and CO 2 emissions. Here, we propose a cheaper alternative: We train Word2Vec on target-domain text and align the resulting word vectors with the wordpiece vectors of a general-domain PTLM. We evaluate on eight English biomedical Named Entity Recognition (NER) tasks and compare against the recently proposed BioBERT model. We cover over 60{\%} of the BioBERT - BERT F1 delta, at 5{\%} of BioBERT{'}s CO 2 footprint and 2{\%} of its cloud compute cost. We also show how to quickly adapt an existing general-domain Question Answering (QA) model to an emerging domain: the Covid-19 pandemic.}
}
@article{blurb_paper,
	title        = {Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing},
	author       = {Yu Gu and Robert Tinn and Hao Cheng and Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann and Jianfeng Gao and Hoifung Poon},
	year         = 2020,
	journal      = {CoRR},
	url          = {https://arxiv.org/abs/2007.15779},
	archiveprefix = {arXiv},
	eprint       = {2007.15779}
}
@article{Reference1,
	title        = {Littrow Configuration Tunable External Cavity Diode Laser with Fixed Direction Output Beam},
	author       = {C. J. Hawthorn and K. P. Weber and R. E. Scholten},
	year         = 2001,
	month        = 12,
	journal      = {Review of Scientific Instruments},
	volume       = 72,
	number       = 12,
	pages        = {4477--4479},
	url          = {http://link.aip.org/link/?RSI/72/4477/1},
	abstract     = {We have developed an enhanced Littrow configuration extended cavity diode laser (ECDL) that can be tuned without changing the direction of the output beam. The output of a conventional Littrow ECDL is reflected from a plane mirror fixed parallel to the tuning diffraction grating. Using a free-space Michelson wavemeter to measure the laser wavelength, we can tune the laser over a range greater than 10 nm without any alteration of alignment.},
	numpages     = 3
}
@article{Reference3,
	title        = {A Simple Extended-Cavity Diode Laser},
	author       = {A. S. Arnold and J. S. Wilson and M. G. Boshier and J. Smith},
	year         = 1998,
	month        = 3,
	journal      = {Review of Scientific Instruments},
	volume       = 69,
	number       = 3,
	pages        = {1236--1239},
	url          = {http://link.aip.org/link/?RSI/69/1236/1},
	abstract     = {Operating a laser diode in an extended cavity which provides frequency-selective feedback is a very effective method of reducing the laser's linewidth and improving its tunability. We have developed an extremely simple laser of this type, built from inexpensive commercial components with only a few minor modifications. A 780~nm laser built to this design has an output power of 80~mW, a linewidth of 350~kHz, and it has been continuously locked to a Doppler-free rubidium transition for several days.},
	numpages     = 4
}
@article{LH:83,
	title        = {A real-time garbage collector based on the lifetimes of objects},
	author       = {Henry Lieberman and Carl Hewitt},
	year         = 1983,
	month        = jun,
	journal      = CACM,
	publisher    = {ACM},
	address      = {New York, New York, USA},
	volume       = 26,
	number       = 6,
	pages        = {419--429},
	doi          = {10.1145/358141.358147},
	issn         = {0001-0782}
}
@inproceedings{Moon:84,
	title        = {Garbage collection in a large {LISP} system},
	author       = {David A. Moon},
	pages        = {235--246},
	doi          = {10.1145/800055.802040},
	crossref     = {LFP:84}
}
@proceedings{LFP:84,
	title        = LFP84,
	year         = 1984,
	month        = aug,
	booktitle    = LFP84,
	location     = {Austin, Texas, USA},
	publisher    = {ACM},
	address      = {New York, New York, USA},
	isbn         = {0-89791-142-3},
	shortname    = {LFP '84}
}
@inproceedings{Ungar:84,
	title        = {Generation Scavenging: A non-disruptive high performance storage reclamation algorithm},
	author       = {David Ungar},
	pages        = {157--167},
	doi          = {10.1145/800020.808261},
	crossref     = {SDE:1}
}
@article{ACM:83,
	author       = {Association for Computing Machinery},
	year         = 1983,
	journal      = {Computing Reviews},
	volume       = 24,
	number       = 11,
	pages        = {503--512}
}
@inproceedings{borsch2011,
	title        = {A Particle Filter algorithm for {B}ayesian Wordsegmentation},
	author       = {Benjamin Borschinger and Mark Johnson},
	year         = 2011,
	month        = {December},
	booktitle    = {Proceedings of the Australasian Language Technology Association Workshop 2011},
	address      = {Canberra, Australia},
	pages        = {10--18}
}
@article{Ando2005,
	title        = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	author       = {Ando, Rie Kubota and Zhang, Tong},
	year         = 2005,
	month        = dec,
	journal      = {Journal of Machine Learning Research},
	publisher    = {JMLR.org},
	volume       = 6,
	pages        = {1817--1853},
	issn         = {1532-4435},
	acmid        = 1194905,
	issue_date   = {12/1/2005},
	numpages     = 37
}
@inproceedings{P16-1001,
	title        = {Noise reduction and targeted exploration in imitation learning for      Abstract Meaning Representation parsing    },
	author       = {Goodman, James and Vlachos, Andreas and Naradowsky, Jason},
	year         = 2016,
	booktitle    = {Proceedings of the 54th Annual Meeting of the Association for      Computational Linguistics (Volume 1: Long Papers)    },
	location     = {Berlin, Germany},
	publisher    = {Association for Computational Linguistics},
	pages        = {1--11},
	doi          = {10.18653/v1/P16-1001},
	url          = {http://aclweb.org/anthology/P16-1001}
}
@inproceedings{C14-1001,
	title        = {Learning from 26 Languages: Program Management and Science in the Babel Program},
	author       = {Harper, Mary},
	year         = 2014,
	booktitle    = {Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers},
	location     = {Dublin, Ireland},
	publisher    = {Dublin City University and Association for Computational Linguistics},
	pages        = 1,
	url          = {http://aclweb.org/anthology/C14-1001}
}
@inproceedings{MacAvaney:Yates:2021,
	title        = {Simplified Data Wrangling with ir\_datasets},
	author       = {MacAvaney, Sean and Yates, Andrew and Feldman, Sergey and Downey, Doug and Cohan, Arman and Goharian, Nazli},
	year         = 2021,
	booktitle    = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
	pages        = {2429--2436}
}
@inproceedings{pyserini,
	title        = {Pyserini: {A P}ython Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations},
	author       = {Lin, Jimmy and Ma, Xueguang and Lin, Sheng-Chieh and Yang, Jheng-Hong and Pradeep, Ronak and Nogueira, Rodrigo},
	year         = 2021,
	booktitle    = {SIGIR},
	pages        = {2356–-2362}
}
@article{A2A:BMC,
	title        = {{A2A: A} platform for research in biomedical literature search},
	author       = {Rybinski, Maceij and Karimi, Sarvnaz and Nguyen, Vincent and Paris, Cecile},
	year         = 2020,
	journal      = {BMC Bioinformatics},
	volume       = 21,
	number       = 572
}
@inproceedings{CSIROmed:2021,
	title        = {{CSIROmed} Team Report of {TREC} 2021 Clinical Trials track: Experiments with {BERT} Reranking Methods},
	author       = {Rybinski, Maciej and Nguyen, Vincent and Karimi, Sarvnaz},
	year         = 2021,
	booktitle    = {TREC},
	address      = {Online}
}
@footnote{Note1,
	note         = {The heat equation mentioned is the one dimensional form. The full form of the heat equation has three spacial dimensions, but is outside the scope of this paper.},
	key          = {Note1}
}
@footnote{Note2,
	note         = {The value of the Stefan-Boltzmann constant is assumed in this paper: $\sigma = 5.670373 \times 10^{-8} \protect \text  { W}\protect \text  {m}^{-2}\protect \text  {K}^{-4}.$},
	key          = {Note2}
}
@footnote{Note3,
	note         = {Convection cooling and heating is sometimes called Newton's Law of Cooling and Heating, in cases where the heat transfer coefficient is independent or relatively independent of the temperature difference between object and environment.},
	key          = {Note3}
}
@footnote{Note4,
	note         = {For a horizontal cylindrical rod, $A_{\circledcirc } = A_{\circleddash }.$ In other words, the surface area of convection is the same as the surface are of radiation for a bare aluminum rod.},
	key          = {Note4}
}
@misc{1,
	title        = {Specific heats and molar heat capacities for various substances at 20 C},
	note         = {Accessed: 2014-07-02},
	howpublished = {\url{http://hyperphysics.phy-astr.gsu.edu/hbase/tables/sphtt.html}}
}
@misc{2,
	title        = {Thermal Conductivity},
	note         = {Accessed: 2014-07-01},
	howpublished = {\url{http://hyperphysics.phy-astr.gsu.edu/hbase/tables/thrcn.html}}
}
@misc{3,
	title        = {Heat Transfer Mechanisms},
	note         = {Accessed: 2014-07-04},
	howpublished = {\url{http://www.engr.colostate.edu/~allan/heat\_trans/page4/page4.html}}
}
@misc{4,
	title        = {Specific heats and molar heat capacities for various substances at 20 C},
	note         = {Accessed: 2014-07-06},
	howpublished = {\url{http://hyperphysics.phy-astr.gsu.edu/hbase/tables/sphtt.html}}
}
@misc{5,
	title        = {Power Resistor Thick Film Technology: LTO100 Power Resistor},
	note         = {Accessed: 2014-07-06},
	howpublished = {\url{http://www.vishay.com/docs/50051/lto100.pdf}}
}
@book{ref1,
	title        = {{The C Programming Language Second Edition}},
	author       = {Kernighan, Brian W. and Ritchie, Dennis M.},
	year         = 1988,
	publisher    = {Prentice-Hall, Inc.}
}
@book{ref2,
	title        = {{Myths and Their Origins Plus Some Extra Words to Make the Title Really Long and Extend to the Next Line}},
	author       = {George P. Burdell},
	year         = 2017,
	publisher    = {Real Books, Inc.}
}
@inproceedings{Dang:2006,
	title        = {DUC 2005: Evaluation of Question-Focused Summarization Systems},
	author       = {Dang, Hoa Trang},
	year         = 2006,
	booktitle    = {Proceedings of the Workshop on Task-Focused Summarization and Question Answering},
	pages        = {48--55},
	abstract     = {The Document Understanding Conference (DUC) 2005 evaluation had a single user-oriented, question-focused summarization task, which was to synthesize from a set of 25-50 documents a well-organized, fluent answer to a complex question. The evaluation shows that the best summarization systems have difficulty extracting relevant sentences in response to complex questions (as opposed to representative sentences that might be appropriate to a generic summary). The relatively generous allowance of 250 words for each answer also reveals how difficult it is for current summarization systems to produce fluent text from multiple documents.},
	mendeley-groups = {Zotero - ARC/ARCEBM2014,Zotero - ARC/ARC2016,Zotero - ARC}
}
@inproceedings{Molla:BioASQ7b,
	title        = {Classification Betters Regression in Query-Based Multi-document Summarisation Techniques for Question Answering},
	author       = {Molla, Diego and Jones, Christopher},
	year         = 2020,
	booktitle    = {Machine Learning and Knowledge Discovery in Databases},
	publisher    = {Springer International Publishing},
	address      = {Cham},
	pages        = {624--635},
	isbn         = {978-3-030-43887-6},
	editor       = {Cellier, Peggy and Driessens, Kurt},
	abstract     = {Task B Phase B of the 2019 BioASQ challenge focuses on biomedical question answering. Macquarie University's participation applies query-based multi-document extractive summarisation techniques to generate a multi-sentence answer given the question and the set of relevant snippets. In past participation we explored the use of regression approaches using deep learning architectures and a simple policy gradient architecture. For the 2019 challenge we experiment with the use of classification approaches with and without reinforcement learning. In addition, we conduct a correlation analysis between various ROUGE metrics and the BioASQ human evaluation scores.}
}
@misc{stablebaselines,
	title        = {Stable Baselines},
	author       = {Hill, Ashley and Raffin, Antonin and Ernestus, Maximilian and Gleave, Adam and Kanervisto, Anssi and Traore, Rene and Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and Radford, Alec and Schulman, John and Sidor, Szymon and Wu, Yuhuai},
	year         = 2018,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/hill-a/stable-baselines}}
}
@article{schulman:2017,
	title        = {Proximal policy optimization algorithms},
	author       = {Schulman, John and Wolski, Filip and Dhariwal, Prafulla and Radford, Alec and Klimov, Oleg},
	year         = 2017,
	journal      = {arXiv preprint arXiv:1707.06347}
}
@article{williams:1992,
	title        = {Simple statistical gradient-following algorithms for connectionist reinforcement learning},
	author       = {Williams, Ronald J},
	year         = 1992,
	journal      = {Machine learning},
	publisher    = {Springer},
	volume       = 8,
	number       = {3-4},
	pages        = {229--256}
}
@mastersthesis{jones:2020,
	title        = {Reinforcement Learning For Query-based Multi-document Extractive Summarisation},
	author       = {Jones, Christopher R},
	year         = 2020,
	month        = jan,
	school       = {Macquarie University}
}
@inproceedings{liu:2019,
	title        = {Text Summarization with Pretrained Encoders},
	author       = {Liu, Yang  and Lapata, Mirella},
	year         = 2019,
	month        = nov,
	booktitle    = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
	address      = {Hong Kong, China},
	pages        = {3730--3740},
	doi          = {10.18653/v1/D19-1387},
	url          = {https://www.aclweb.org/anthology/D19-1387}
}
@inproceedings{lewis:2020,
	title        = {{BART}: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension},
	author       = {Lewis, Mike  and Liu, Yinhan  and Goyal, Naman  and Ghazvininejad, Marjan  and Mohamed, Abdelrahman  and Levy, Omer  and Stoyanov, Veselin  and Zettlemoyer, Luke},
	year         = 2020,
	month        = jul,
	booktitle    = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
	address      = {Online},
	pages        = {7871--7880},
	url          = {https://www.aclweb.org/anthology/2020.acl-main.703}
}
@inproceedings{cheng:2016,
	title        = {Neural Summarization by Extracting Sentences and Words},
	author       = {Cheng, Jianpeng  and Lapata, Mirella},
	year         = 2016,
	month        = aug,
	booktitle    = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
	address      = {Berlin, Germany},
	pages        = {484--494},
	doi          = {10.18653/v1/P16-1046},
	url          = {https://www.aclweb.org/anthology/P16-1046}
}
@inproceedings{see:manning:2017,
	title        = {Get To The Point: Summarization with Pointer-Generator Networks},
	author       = {See, Abigail  and Liu, Peter J.  and Manning, Christopher D.},
	year         = 2017,
	month        = jul,
	booktitle    = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
	address      = {Vancouver, Canada},
	pages        = {1073--1083},
	doi          = {10.18653/v1/P17-1099},
	url          = {https://www.aclweb.org/anthology/P17-1099}
}
@inproceedings{mekala:2020,
	title        = {Contextualized Weak Supervision for Text Classification},
	author       = {Mekala, Dheeraj  and Shang, Jingbo},
	year         = 2020,
	month        = jul,
	booktitle    = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
	address      = {Online},
	pages        = {323--333},
	url          = {https://www.aclweb.org/anthology/2020.acl-main.30}
}
@inproceedings{bioasq7:2019,
	title        = {Results of the Seventh Edition of the BioASQ Challenge},
	author       = {Nentidis, Anastasios and Bougiatiotis, Konstantinos and Krithara, Anastasia and Paliouras, Georgios},
	year         = 2020,
	booktitle    = {Machine Learning and Knowledge Discovery in Databases},
	publisher    = {Springer International Publishing},
	address      = {Cham},
	pages        = {553--568},
	isbn         = {978-3-030-43887-6},
	editor       = {Cellier, Peggy and Driessens, Kurt},
	abstract     = {The results of the seventh edition of the BioASQ challenge are presented in this paper. The aim of the BioASQ challenge is the promotion of systems and methodologies through the organization of a challenge on the tasks of large-scale biomedical semantic indexing and question answering. In total, 30 teams with more than 100 systems participated in the challenge this year. As in previous years, the best systems were able to outperform the strong baselines. This suggests that state-of-the-art systems are continuously improving, pushing the frontier of research.}
}
@book{Smith:2012qr,
	title        = {{B}ook {T}itle},
	author       = {Smith, J.~M. and Jones, A.~B.},
	year         = 2012,
	publisher    = {Publisher},
	edition      = {7th}
}
@article{Linhart2014,
	title        = {Teaching Writing and Communication in a Mathematical Modeling Course},
	author       = {Jean Marie Linhart},
	year         = 2014,
	journal      = {PRIMUS},
	volume       = 24,
	number       = 7,
	pages        = {594--607}
}
@book{higham1998handbook,
	title        = {Handbook of writing for the mathematical sciences},
	author       = {Higham, N.J.},
	year         = 1998,
	publisher    = {Society for Industrial Mathematics}
}
@misc{vihart,
	title        = {Pi is (still) wrong},
	author       = {Hart, V. I.},
	year         = 2011,
	publisher    = {YouTube},
	url          = {http://vihart.com/blog/pi-is-still-wrong/}
}
@misc{taumanifesto,
	title        = {The {T}au {M}anifesto},
	author       = {Michael Hartl},
	year         = 2010,
	url          = {http://tauday.com/}
}
@misc{chaoswiki,
	title        = {Chaos {T}heory},
	author       = {Wikipedia},
	year         = 2012,
	url          = {http://en.wikipedia.org/Chaos\_theory}
}

@misc{newsguard_misinformation_traffic,
    title = {Sizing the Infodemic: NewsGuard Analysts Have Now Found More than 500 ‘News’ Sites Peddling COVID-19 Misinformation and Identified 50 Hoaxes Relating to the COVID-19 Vaccines},
    author = {NewsGuard},
    year=2021,
    url = {https://www.newsguardtech.com/press/newsguard-finds-more-than-500-sites-50-covid-vaccine-myths/}
}

@misc{fractalwiki,
	title        = {Fractal},
	author       = {Wikipedia},
	year         = 2012,
	url          = {http://en.wikipedia.org/Fractal}
}
@inproceedings{Qu:Arguello:Wang:2021,
	title        = {A Deep Analysis of an Explainable Retrieval Model for Precision Medicine Literature Search},
	author       = {Qu, Jiaming and Arguello, Jaime and Wang, Yue},
	year         = 2021,
	booktitle    = {Advances in  Information Retrieval},
	pages        = {544--557}
}
@inproceedings{Qu:Arguello:Wang:20220,
	title        = {Towards Explainable Retrieval Models for Precision Medicine Literature Search},
	author       = {Qu, Jiaming and Arguello, Jaime and Wang, Yue},
	year         = 2020,
	booktitle    = {SIGIR},
	pages        = {1593–1596},
	doi          = {10.1145/3397271.3401277},
	url          = {https://doi.org/10.1145/3397271.3401277},
	keywords     = {medical information retrieval, explainable retrieval model, structured relevance feedback}
}
@inproceedings{Rybinski:Watts:Karimi:2022,
	title        = {{A2A-API}: A Prototype for Biomedical Information Retrieval Research and Benchmarking},
	author       = {Rybinski, Maciej and Watts, Liam and Karimi, Sarvnaz},
	year         = 2022,
	booktitle    = {SIGIR},
	pages        = {3318--3322},
	doi          = {10.1145/3477495.3531667},
	url          = {https://doi.org/10.1145/3477495.3531667}
}
@inproceedings{Turian:2010,
	title        = {Word representations: a simple and general method for semi-supervised learning},
	author       = {Joseph Turian, Lev Ratinov, and Yoshua Bengio},
	year         = 2010,
	booktitle    = {The 48th annual meeting of the association for computational linguistics},
	pages        = {384–394}
}
@article{search-like-an-expert-2022,
	title        = {Search like an expert: Reducing expertise disparity using a hybrid neural index for COVID-19 queries},
	author       = {Vincent Nguyen and Maciej Rybinski and Sarvnaz Karimi and Zhenchang Xing},
	year         = 2022,
	journal      = {Journal of Biomedical Informatics},
	volume       = 127,
	pages        = 104005,
	doi          = {https://doi.org/10.1016/j.jbi.2022.104005},
	issn         = {1532-0464},
	url          = {https://www.sciencedirect.com/science/article/pii/S1532046422000211},
	keywords     = {COVID-19, Universal sentence embeddings, Information retrieval, Natural language processing, Neural index, Dense retrieval, Medical misinformation, Biomedical search},
	abstract     = {Consumers from non-medical backgrounds often look for information regarding a specific medical information need; however, they are limited by their lack of medical knowledge and may not be able to find reputable resources. As a case study, we investigate reducing this knowledge barrier to allow consumers to achieve search effectiveness comparable to that of an expert, or a medical professional, for COVID-19 related questions. We introduce and evaluate a hybrid index model that allows a consumer to formulate queries using consumer language to find relevant answers to COVID-19 questions. Our aim is to reduce performance degradation between medical professional queries and those of a consumer. We use a universal sentence embedding model to project consumer queries into the same semantic space as professional queries. We then incorporate sentence embeddings into a search framework alongside an inverted index. Documents from this index are retrieved using a novel scoring function that considers sentence embeddings and BM25 scoring. We find that our framework alleviates the expertise disparity, which we validate using an additional set of crowdsourced—consumer—queries even in an unsupervised setting. We also propose an extension of our method, where the sentence encoder is optimised in a supervised setup. Our framework allows for a consumer to search using consumer queries to match the search performance with that of a professional.}
}

@article{self-diagnosis,
    author = {Kuehn, Bridget M.},
    title = "{More Than One-Third of US Individuals Use the Internet to Self-diagnose}",
    journal = {Journal of the American Medical Association},
    volume = {309},
    number = {8},
    pages = {756-757},
    year = {2013},
    month = {02},
    abstract = "{More than one-third of US individuals turn to the Internet to diagnose health problems, according to results from Health Online 2013, a survey (http://tinyurl.com/blqudew) of the online health behavior of Americans conducted by the Pew Research Center's Internet and American Life Project. Slightly more than half of those who reported using the Internet to research health problems discussed what they found with a physician.The survey, which involved telephone interviews with a nationally representative sample of more than 3000 US adults, is the latest from the Pew Research Center to document how Americans are increasingly seeking health and medical information online. It provides a snapshot of how patients' online behaviors are evolving as the web doesâ€”for instance, as a substantial number of individuals adopt mobile technology and as websites require readers to pay to access health information.}",
    issn = {0098-7484},
    doi = {10.1001/jama.2013.629},
    url = {https://doi.org/10.1001/jama.2013.629},
    eprint = {https://jamanetwork.com/journals/jama/articlepdf/1656251/jmn130006\_756\_757.pdf},
}

@ARTICLE{Evidence-based-approach,
  title    = "Evidence-based approach to the medical literature",
  author   = "Fletcher, Robert and Fletcher, Suzanne",
  journal  = "Journal General Internal Medicine",
  volume   = "12 Suppl 2",
  pages    = "S5--14",
  month    =  apr,
  year     =  1997,
  language = "en"
}

@ARTICLE{pubmed-as-resource,
  title     = "Exploring {PubMed} as a reliable resource for scholarly
               communications services",
  author    = "Ossom Williamson, Peace and Minter, Christian I J",
  journal   = "Journal of the Medical Library Association",
  publisher = "University Library System, University of Pittsburgh",
  volume    =  107,
  number    =  1,
  pages     = "16--29",
  month     =  jan,
  year      =  2019,
  language  = "en"
}

@article{medical_image_segmentation_review,
title = {Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation},
journal = {Medical Image Analysis},
volume = {63},
pages = {101693},
year = {2020},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2020.101693},
url = {https://www.sciencedirect.com/science/article/pii/S136184152030058X},
author = {Nima Tajbakhsh and Laura Jeyaseelan and Qian Li and Jeffrey N. Chiang and Zhihao Wu and Xiaowei Ding},
keywords = {Medical image segmentation, Imperfect dataset, Scarce annotations, Noisy annotations, Unreliable annotations, Sparse annotations, And weak annotations},
}

@inproceedings{bioreddit,
    title = "{B}io{R}eddit: Word Embeddings for User-Generated Biomedical {NLP}",
    author = "Basaldella, Marco  and
      Collier, Nigel",
    booktitle = "LOUHI",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    url = "https://aclanthology.org/D19-6205",
    doi = "10.18653/v1/D19-6205",
    pages = "34--38",
}

@inproceedings{mashqa,
    title = "Question Answering with Long Multiple-Span Answers",
    author = "Zhu, Ming  and
      Ahuja, Aman  and
      Juan, Da-Cheng  and
      Wei, Wei  and
      Reddy, Chandan K.",
    booktitle = "EMNLP (Findings)",
    month = nov,
    year = "2020",
    address = "Online",
    url = "https://aclanthology.org/2020.findings-emnlp.342",
    doi = "10.18653/v1/2020.findings-emnlp.342",
    pages = "3840--3849",
    abstract = "Answering questions in many real-world applications often requires complex and precise information excerpted from texts spanned across a long document. However, currently no such annotated dataset is publicly available, which hinders the development of neural question-answering (QA) systems. To this end, we present MASH-QA, a Multiple Answer Spans Healthcare Question Answering dataset from the consumer health domain, where answers may need to be excerpted from multiple, non-consecutive parts of text spanned across a long document. We also propose MultiCo, a neural architecture that is able to capture the relevance among multiple answer spans, by using a query-based contextualized sentence selection approach, for forming the answer to the given question. We also demonstrate that conventional QA models are not suitable for this type of task and perform poorly in this setting. Extensive experiments are conducted, and the experimental results confirm the proposed model significantly outperforms the state-of-the-art QA models in this multi-span QA setting.",
}

@inproceedings{meqsum,
    title = "On the Summarization of Consumer Health Questions",
    author = "Ben Abacha, Asma  and
      Demner-Fushman, Dina",
    booktitle = "ACL",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    url = "https://aclanthology.org/P19-1215",
    doi = "10.18653/v1/P19-1215",
    pages = "2228--2234",
    abstract = "Question understanding is one of the main challenges in question answering. In real world applications, users often submit natural language questions that are longer than needed and include peripheral information that increases the complexity of the question, leading to substantially more false positives in answer retrieval. In this paper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of 1,000 summarized consumer health questions. We explore data augmentation methods and evaluate state-of-the-art neural abstractive models on this new task. In particular, we show that semantic augmentation from question datasets improves the overall performance, and that pointer-generator networks outperform sequence-to-sequence attentional models on this task, with a ROUGE-1 score of 44.16{\%}. We also present a detailed error analysis and discuss directions for improvement that are specific to question summarization.",
}

@ARTICLE{cMedQAv2,
  author={Zhang, Sheng and Zhang, Xin and Wang, Hui and Guo, Lixiang and Liu, Shanshan},
  journal={IEEE Access}, 
  title={Multi-Scale Attentive Interaction Networks for Chinese Medical Question Answer Selection}, 
  year={2018},
  volume={6},
  number={},
  pages={74061-74071},
  doi={10.1109/ACCESS.2018.2883637}}

  @inproceedings{chimed,
    title = "{C}hi{M}ed: A {C}hinese Medical Corpus for Question Answering",
    author = "Tian, Yuanhe  and
      Ma, Weicheng  and
      Xia, Fei  and
      Song, Yan",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    url = "https://aclanthology.org/W19-5027",
    doi = "10.18653/v1/W19-5027",
    pages = "250--260",
    abstract = "Question answering (QA) is a challenging task in natural language processing (NLP), especially when it is applied to specific domains. While models trained in the general domain can be adapted to a new target domain, their performance often degrades significantly due to domain mismatch. Alternatively, one can require a large amount of domain-specific QA data, but such data are rare, especially for the medical domain. In this study, we first collect a large-scale Chinese medical QA corpus called ChiMed; second we annotate a small fraction of the corpus to check the quality of the answers; third, we extract two datasets from the corpus and use them for the relevancy prediction task and the adoption prediction task. Several benchmark models are applied to the datasets, producing good results for both tasks.",
}


@article{multiplenegativesrankingloss,
  author    = {Matthew L. Henderson and
               Rami Al{-}Rfou and
               Brian Strope and
               Yun{-}Hsuan Sung and
               L{\'{a}}szl{\'{o}} Luk{\'{a}}cs and
               Ruiqi Guo and
               Sanjiv Kumar and
               Balint Miklos and
               Ray Kurzweil},
  title     = {Efficient Natural Language Response Suggestion for Smart Reply},
  journal   = {CoRR},
  volume    = {abs/1705.00652},
  year      = {2017},
  url       = {http://arxiv.org/abs/1705.00652},
  eprinttype = {arXiv},
  eprint    = {1705.00652},
  timestamp = {Thu, 30 Apr 2020 11:17:18 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/HendersonASSLGK17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{gpt_3_vs_t5,
    title = "What Makes Good In-Context Examples for {GPT}-3?",
    author = "Liu, Jiachang  and
      Shen, Dinghan  and
      Zhang, Yizhe  and
      Dolan, Bill  and
      Carin, Lawrence  and
      Chen, Weizhu",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = may,
    year = "2022",
    address = "Dublin, Ireland and Online",
    url = "https://aclanthology.org/2022.deelio-1.10",
    doi = "10.18653/v1/2022.deelio-1.10",
    pages = "100--114",
    abstract = "GPT-3 has attracted lots of attention due to its superior performance across a wide range of NLP tasks, especially with its in-context learning abilities. Despite its success, we found that the empirical results of GPT-3 depend heavily on the choice of in-context examples. In this work, we investigate whether there are more effective strategies for judiciously selecting in-context examples (relative to random sampling) that better leverage GPT-3{'}s in-context learning capabilities.Inspired by the recent success of leveraging a retrieval module to augment neural networks, we propose to retrieve examples that are semantically-similar to a test query sample to formulate its corresponding prompt. Intuitively, the examples selected with such a strategy may serve as more informative inputs to unleash GPT-3{'}s power of text generation. We evaluate the proposed approach on several natural language understanding and generation benchmarks, where the retrieval-based prompt selection approach consistently outperforms the random selection baseline. Moreover, it is observed that the sentence encoders fine-tuned on task-related datasets yield even more helpful retrieval results. Notably, significant gains are observed on tasks such as table-to-text generation (44.3{\%} on the ToTTo dataset) and open-domain question answering (45.5{\%} on the NQ dataset).",
}

@inproceedings{gpt_3_paper,
 author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {1877--1901},
 publisher = {Curran Associates, Inc.},
 title = {Language Models are Few-Shot Learners},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf},
 volume = {33},
 year = {2020}
}


@inproceedings{bert_bm25_orthogonal,
author = {Wang, Shuai and Zhuang, Shengyao and Zuccon, Guido},
title = {{BERT}-Based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval},
year = {2021},
isbn = {9781450386111},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3471158.3472233},
doi = {10.1145/3471158.3472233},
abstract = {The integration of pre-trained deep language models, such as BERT, into retrieval and ranking pipelines has shown to provide large effectiveness gains over traditional bag-of-words models in the passage retrieval task. However, the best setup for integrating such deep language models is still unclear. When BERT is used to re-rank passages (i.e., BERT re-ranker), previous work has empirically shown that, while in practice BERT re-ranker cannot act as initial retriever due to BERT's high query time costs, and thus a bag-of-words model such as BM25 is required. It is not necessary to interpolate BERT re-ranker and bag-of-words scores to generate the final ranking. In fact, the BERT re-ranker scores alone can be used by the re-ranker: the BERT re-ranker score appears to already capture the relevance signal provided by BM25. In this paper, we further investigate the topic of interpolating BM25 and BERT-based rankers. Unlike previous work that considered the BERT re-ranker, however, here we consider BERT-based dense retrievers (RepBERT and ANCE). Dense retrievers encode queries and documents into low dimensional BERT-based embeddings. These methods overcome BERT's high computational costs at query time, and can thus be feasibly used in practice as whole-collection retrievers, rather than just as re-rankers. Our novel empirical findings suggest that, unlike for BERT re-ranker, interpolation with BM25 is necessary for BERT-based dense retrievers to perform effectively; and the gains provided by the interpolation are significant. Further analysis reveals why this is so: dense retrievers are very effective at encoding strong relevance signals, but they fail in identifying weaker relevance signals -- a task that the interpolation with BM25 is able to make up for.},
booktitle = {Proceedings of the International Conference on the Theory of Information Retrieval},
pages = {317–324},
numpages = {8},
keywords = {neural IR, BERT ranking, passage retrieval, dense retrievers},
location = {Virtual Event, Canada},
}

@article{t5_paper,
  author  = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
  title   = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {140},
  pages   = {1--67},
  url     = {http://jmlr.org/papers/v21/20-074.html}
}

@ARTICLE{covid_misinformation_2020,
  title    = "The danger of misinformation in the {COVID-19} crisis",
  author   = "Nelson, Taylor and Kagan, Nicole and Critchlow, Claire and
              Hillard, Alan and Hsu, Albert",
  journal  = "Mo. Med.",
  volume   =  117,
  number   =  6,
  pages    = "510--512",
  month    =  nov,
  year     =  2020,
  language = "en",
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721433/}
}

@ARTICLE{misinformaton_vaccine_hesistancy_social_media_2021,
  title     = "Online misinformation and vaccine hesitancy",
  author    = "Garett, Renee and Young, Sean D",
  journal   = "Transl. Behav. Med.",
  publisher = "Oxford University Press (OUP)",
  volume    =  11,
  number    =  12,
  pages     = "2194--2199",
  month     =  dec,
  year      =  2021,
  keywords  = "COVID-19; Media; Misinformation; Vaccine; Vaccine hesitancy",
  copyright = "https://academic.oup.com/journals/pages/open\_access/funder\_policies/chorus/standard\_publication\_model",
  language  = "en",
  url = {https://pubmed.ncbi.nlm.nih.gov/34529080/}
}

@ARTICLE{health-literacy-developed-countries,
  title    = "Health literacy interventions and outcomes: an updated systematic
              review",
  author   = "Berkman, Nancy D and Sheridan, Stacey L and Donahue, Katrina E
              and Halpern, David J and Viera, Anthony and Crotty, Karen and
              Holland, Audrey and Brasure, Michelle and Lohr, Kathleen N and
              Harden, Elizabeth and Tant, Elizabeth and Wallace, Ina and
              Viswanathan, Meera",
  journal  = "Evid. Rep. Technol. Assess. (Full Rep.)",
  number   =  199,
  pages    = "1--941",
  month    =  mar,
  year     =  2011,
  language = "en"
}

@article{diverse_beam_search, title={Diverse Beam Search for Improved Description of Complex Scenes}, volume={32}, url={https://ojs.aaai.org/index.php/AAAI/article/view/12340}, DOI={10.1609/aaai.v32i1.12340}, abstractNote={ &lt;p&gt; A single image captures the appearance and position of multiple entities in a scene as well as their complex interactions. As a consequence, natural language grounded in visual contexts tends to be diverse---with utterances differing as focus shifts to specific objects, interactions, or levels of detail. Recently, neural sequence models such as RNNs and LSTMs have been employed to produce visually-grounded language. Beam Search, the standard work-horse for decoding sequences from these models, is an approximate inference algorithm that decodes the top-B sequences in a greedy left-to-right fashion. In practice, the resulting sequences are often minor rewordings of a common utterance, failing to capture the multimodal nature of source images. To address this shortcoming, we propose Diverse Beam Search (DBS), a diversity promoting alternative to BS for approximate inference. DBS produces sequences that are significantly different from each other by incorporating diversity constraints within groups of candidate sequences during decoding; moreover, it achieves this with minimal computational or memory overhead. We demonstrate that our method improves both diversity and quality of decoded sequences over existing techniques on two visually-grounded language generation tasks---image captioning and visual question generation---particularly on complex scenes containing diverse visual content. We also show similar improvements at language-only machine translation tasks, highlighting the generality of our approach. &lt;/p&gt; }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Vijayakumar, Ashwin and Cogswell, Michael and Selvaraju, Ramprasaath and Sun, Qing and Lee, Stefan and Crandall, David and Batra, Dhruv}, year={2018}, month={Apr.} }

@ARTICLE{dr_google_improves_patient_relationship,
  title     = "The effect of Dr Google on doctor-patient encounters in primary
               care: a quantitative, observational, cross-sectional study",
  author    = "Van Riel, Noor and Auwerx, Koen and Debbaut, Pieterjan and Van
               Hees, Sanne and Schoenmakers, Birgitte",
  abstract  = "BACKGROUND: Two-thirds of all patients search the internet prior
               to a health consultation. AIM: To explore how searching for
               online health information before visiting a doctor influences
               patients' behaviour during the consultation. DESIGN \& SETTING:
               A quantitative, observational, and cross-sectional study of
               18-75-year-old patients who used the internet. METHOD: Patients
               were recruited by social media for the quantitative study. This
               was followed by a qualitiative study of GPs who were questioned
               in focus groups. Two questions were addressed: What is the
               effect of searching online health information on the behaviour
               of the patients? How does the GP handle this information?
               RESULTS: Almost half of all responders (total n = 963) usually
               went to the doctor after the online information search but
               two-thirds were not reassured by the internet search. More than
               half of responders had more confidence in their GP after
               searching online. The older the responders, the more they went
               to the doctor after their internet search and the younger the
               responders, the more they were worried. The more frequently
               people consulted the internet for specific complaints, the more
               likely they reported reassurance. DISCUSSION: Patients usually
               made an appointment with their GP after the internet search. New
               symptoms are rarely noticed and the search usually did not lead
               patients to distrust their GP. The majority of GPs described
               positive effects of the online search behaviour on the
               consultation. CONCLUSION: The emerging use of the internet for
               searching health information, commonly referred to as 'Dr
               Google', is not seen as a threat by GPs and leads to a better
               mutual understanding of symptoms and diagnosis.",
  journal   = "BJGP Open",
  publisher = "Royal College of General Practitioners",
  volume    =  1,
  number    =  2,
  pages     = "bjgpopen17X100833",
  month     =  may,
  year      =  2017,
  keywords  = "health care; patient education; patient empowerment; primary
               care",
  language  = "en",
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6169945/}
}

@InProceedings{bioasq_2022,
author="Nentidis, Anastasios
and Katsimpras, Georgios
and Vandorou, Eirini
and Krithara, Anastasia
and Miranda-Escalada, Antonio
and Gasco, Luis
and Krallinger, Martin
and Paliouras, Georgios",
editor="Barr{\'o}n-Cede{\~{n}}o, Alberto
and Da San Martino, Giovanni
and Degli Esposti, Mirko
and Sebastiani, Fabrizio
and Macdonald, Craig
and Pasi, Gabriella
and Hanbury, Allan
and Potthast, Martin
and Faggioli, Guglielmo
and Ferro, Nicola",
title="Overview of {B}io{ASQ} 2022: The Tenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering",
booktitle="Experimental IR Meets Multilinguality, Multimodality, and Interaction",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="337--361",
abstract="This paper presents an overview of the tenth edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2022. BioASQ is an ongoing series of challenges that promotes advances in the domain of large-scale biomedical semantic indexing and question answering. In this edition, the challenge was composed of the three established tasks a, b and Synergy, and a new task named DisTEMIST for automatic semantic annotation and grounding of diseases from clinical content in Spanish, a key concept for semantic indexing and search engines of literature and clinical records. This year, BioASQ received more than 170 distinct systems from 38 teams in total for the four different tasks of the challenge. As in previous years, the majority of the competing systems outperformed the strong baselines, indicating the continuous advancement of the state-of-the-art in this domain.",
isbn="978-3-031-13643-6",
url = {https://link.springer.com/chapter/10.1007/978-3-031-13643-6_22}
}

@article{natural_questions_dataset,
title	= {Natural Questions: a Benchmark for Question Answering Research},
author	= {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov},
year	= {2019},
journal	= {Transactions of the Association of Computational Linguistics}
}

@article{adafactor,
  author    = {Noam Shazeer and
               Mitchell Stern},
  title     = {Adafactor: Adaptive Learning Rates with Sublinear Memory Cost},
  journal   = {CoRR},
  volume    = {abs/1804.04235},
  year      = {2018},
  url       = {http://arxiv.org/abs/1804.04235},
  eprinttype = {arXiv},
  eprint    = {1804.04235},
  timestamp = {Mon, 13 Aug 2018 16:48:15 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1804-04235.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{shaps,
title = {A Unified Approach to Interpreting Model Predictions},
author = {Lundberg, Scott M and Lee, Su-In},
booktitle = {NIPS},
editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {4765--4774},
year = {2017},
url = {http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf}
}

@article{mednli,
author={Shivade, Chaitanya},
title = {MedNLI - A Natural Language Inference Dataset For The Clinical Domain (version 1.0.0},
publisher = {PhysioNet},
journal = {PhysioNet},
year = {2019},
url = {https://doi.org/10.13026/C2RS98},
}

@article{pubmedbert,
author = {Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung},
title = {Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing},
year = {2021},
issue_date = {January 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {3},
number = {1},
issn = {2691-1957},
url = {https://doi.org/10.1145/3458754},
doi = {10.1145/3458754},
abstract = {Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. In this article, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. To facilitate this investigation, we compile a comprehensive biomedical NLP benchmark from publicly available datasets. Our experiments show that domain-specific pretraining serves as a solid foundation for a wide range of biomedical NLP tasks, leading to new state-of-the-art results across the board. Further, in conducting a thorough evaluation of modeling choices, both for pretraining and task-specific fine-tuning, we discover that some common practices are unnecessary with BERT models, such as using complex tagging schemes in named entity recognition. To help accelerate research in biomedical NLP, we have released our state-of-the-art pretrained and task-specific models for the community, and created a leaderboard featuring our BLURB benchmark (short for Biomedical Language Understanding &amp; Reasoning Benchmark) at .},
journal = {ACM Trans. Comput. Healthcare},
month = {oct},
articleno = {2},
numpages = {23},
keywords = {domain-specific pretraining, NLP, Biomedical}
}

@ARTICLE{semantic_scholar,
       author = {{Kinney}, Rodney and {Anastasiades}, Chloe and {Authur}, Russell and {Beltagy}, Iz and {Bragg}, Jonathan and {Buraczynski}, Alexandra and {Cachola}, Isabel and {Candra}, Stefan and {Chandrasekhar}, Yoganand and {Cohan}, Arman and {Crawford}, Miles and {Downey}, Doug and {Dunkelberger}, Jason and {Etzioni}, Oren and {Evans}, Rob and {Feldman}, Sergey and {Gorney}, Joseph and {Graham}, David and {Hu}, Fangzhou and {Huff}, Regan and {King}, Daniel and {Kohlmeier}, Sebastian and {Kuehl}, Bailey and {Langan}, Michael and {Lin}, Daniel and {Liu}, Haokun and {Lo}, Kyle and {Lochner}, Jaron and {MacMillan}, Kelsey and {Murray}, Tyler and {Newell}, Chris and {Rao}, Smita and {Rohatgi}, Shaurya and {Sayre}, Paul and {Shen}, Zejiang and {Singh}, Amanpreet and {Soldaini}, Luca and {Subramanian}, Shivashankar and {Tanaka}, Amber and {Wade}, Alex D. and {Wagner}, Linda and {Wang}, Lucy Lu and {Wilhelm}, Chris and {Wu}, Caroline and {Yang}, Jiangjiang and {Zamarron}, Angele and {Van Zuylen}, Madeleine and {Weld}, Daniel S.},
        title = "{The Semantic Scholar Open Data Platform}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Digital Libraries, Computer Science - Computation and Language},
         year = 2023,
        month = jan,
          eid = {arXiv:2301.10140},
        pages = {arXiv:2301.10140},
          doi = {10.48550/arXiv.2301.10140},
archivePrefix = {arXiv},
       eprint = {2301.10140},
 primaryClass = {cs.DL},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230110140K},
}

@ARTICLE{jina_bert,
       author = {{G{\"u}nther}, Michael and {Ong}, Jackmin and {Mohr}, Isabelle and {Abdessalem}, Alaeddine and {Abel}, Tanguy and {Kalim Akram}, Mohammad and {Guzman}, Susana and {Mastrapas}, Georgios and {Sturua}, Saba and {Wang}, Bo and {Werk}, Maximilian and {Wang}, Nan and {Xiao}, Han},
        title = "{Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, 68T50, I.2.7},
         year = 2023,
        month = oct,
          eid = {arXiv:2310.19923},
        pages = {arXiv:2310.19923},
          doi = {10.48550/arXiv.2310.19923},
archivePrefix = {arXiv},
       eprint = {2310.19923},
 primaryClass = {cs.CL},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv231019923G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@inproceedings{caglayan-etal-2020-curious-automatic-metric-bad,
    title = "Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale",
    author = "Caglayan, Ozan  and
      Madhyastha, Pranava  and
      Specia, Lucia",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.210",
    doi = "10.18653/v1/2020.coling-main.210",
    pages = "2322--2328",
    abstract = "Automatic evaluation of language generation systems is a well-studied problem in Natural Language Processing. While novel metrics are proposed every year, a few popular metrics remain as the de facto metrics to evaluate tasks such as image captioning and machine translation, despite their known limitations. This is partly due to ease of use, and partly because researchers expect to see them and know how to interpret them. In this paper, we urge the community for more careful consideration of how they automatically evaluate their models by demonstrating important failure cases on multiple datasets, language pairs and tasks. Our experiments show that metrics (i) usually prefer system outputs to human-authored texts, (ii) can be insensitive to correct translations of rare words, (iii) can yield surprisingly high scores when given a single sentence as system output for the entire test set.",
}

@inproceedings{saadany-orasan-2021-bleu-automatic-metric-bad,
    title = "{BLEU}, {METEOR}, {BERTS}core: Evaluation of Metrics Performance in Assessing Critical Translation Errors in Sentiment-Oriented Text",
    author = "Saadany, Hadeel  and
      Orasan, Constantin",
    editor = "Mitkov, Ruslan  and
      Sosoni, Vilelmini  and
      Gigu{\`e}re, Julie Christine  and
      Murgolo, Elena  and
      Deysel, Elizabeth",
    booktitle = "Proceedings of the Translation and Interpreting Technology Online Conference",
    month = jul,
    year = "2021",
    address = "Held Online",
    publisher = "INCOMA Ltd.",
    url = "https://aclanthology.org/2021.triton-1.6",
    pages = "48--56",
    abstract = "Social media companies as well as censorship authorities make extensive use of artificial intelligence (AI) tools to monitor postings of hate speech, celebrations of violence or profanity. Since AI software requires massive volumes of data to train computers, automatic-translation of the online content is usually implemented to compensate for the scarcity of text in some languages. However, machine translation (MT) mistakes are a regular occurrence when translating sentiment-oriented user-generated content (UGC), especially when a low-resource language is involved. In such scenarios, the adequacy of the whole process relies on the assumption that the translation can be evaluated correctly. In this paper, we assess the ability of automatic quality metrics to detect critical machine translation errors which can cause serious misunderstanding of the affect message. We compare the performance of three canonical metrics on meaningless translations as compared to meaningful translations with a critical error that distorts the overall sentiment of the source text. We demonstrate the need for the fine-tuning of automatic metrics to make them more robust in detecting sentiment critical errors.",
}

@inproceedings{liu-etal-2023-g-eval,
    title = "{G}-Eval: {NLG} Evaluation using Gpt-4 with Better Human Alignment",
    author = "Liu, Yang  and
      Iter, Dan  and
      Xu, Yichong  and
      Wang, Shuohang  and
      Xu, Ruochen  and
      Zhu, Chenguang",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    url = "https://aclanthology.org/2023.emnlp-main.153",
    doi = "10.18653/v1/2023.emnlp-main.153",
    pages = "2511--2522",
    abstract = "The quality of texts generated by natural language generation (NLG) systems is hard to measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE, have been shown to have relatively low correlation with human judgments, especially for tasks that require creativity and diversity. Recent studies suggest using large language models (LLMs) as reference-free metrics for NLG evaluation, which have the benefit of being applicable to new tasks that lack human references. However, these LLM-based evaluators still have lower human correspondence than medium-size neural evaluators. In this work, we present G-Eval, a framework of using large language models with chain-of-thoughts (CoT) and a form-filling paradigm, to assess the quality of NLG outputs. We experiment with two generation tasks, text summarization and dialogue generation. We show that G-Eval with GPT-4 as the backbone model achieves a Spearman correlation of 0.514 with human on summarization task, outperforming all previous methods by a large margin. We also propose analysis on the behavior of LLM-based evaluators, and highlight the potential concern of LLM-based evaluators having a bias towards the LLM-generated texts.",
}

@article{likert1932technique,
  title={A technique for the measurement of attitudes.},
  author={Likert, Rensis},
  journal={Archives of psychology},
  year={1932},
  volume={22},
  number={140},
  pages={55},
  URL = {https://psycnet.apa.org/record/1933-01885-001}
}

@inproceedings{sakuntaladewi2020adat_adaptation_community,
  title={Adat community and climate change adaptation measures: case of Kasepuhan Karang},
  author={Sakuntaladewi, N and Sumirat, BK and others},
  booktitle={IOP Conference Series: Earth and Environmental Science},
  volume={487},
  number={1},
  pages={012005},
  year={2020},
  organization={IOP Publishing}
}


@Article{su11071921_climate_adaptation_farmer,
AUTHOR = {Bate, Bate Godwill and Kimengsi, Jude Ndzifon and Amawa, Sani Gur},
TITLE = {Determinants and Policy Implications of Farmers’ Climate Adaptation Choices in Rural Cameroon},
JOURNAL = {Sustainability},
VOLUME = {11},
YEAR = {2019},
NUMBER = {7},
ARTICLE-NUMBER = {1921},
URL = {https://www.mdpi.com/2071-1050/11/7/1921},
ISSN = {2071-1050},
ABSTRACT = {The issue of climate change and its related impacts is now a social reality. The paradigm shift today in climate change issues is focused on mitigation and adaptation. Besides mitigation, adaptation is considered as an essential strategy for reducing the severity and cost of climate change impacts given the fact that additional future climate change is considered as being inevitable. In this paper, we analyze household socio-economic determinants of climate change adaptation and their policy implications. A survey of 130 farmers in four farm communities in Cameroon was conducted to capture the determinants of farmers’ adaptation to climate change. We employed the binary Logistic regression model to assess the determinants of climate adaptation. Results reveal that in the midst of climate change, 78.33% of farmers have adopted rainfall-related adaptation while 63.33% have resorted to temperature-related adaptation. Based on the binary logistic regression, access to road, access to non-farm income source, and membership of farmers’ groups were significant determinants for the adoption of temperature-related adaptation options. Furthermore, access to improved seeds was found to be the lone significant determinant for the adoption of rainfall-related adaptation options. All in all, much is required to strengthen farmers’ adaptive capacity and increase the range of adaptation options undertaken. As such, policies geared towards building farmers’ resilience should effectively capture the following tri-factors: provision of access roads linking farm communities to nearby urban centres, upscaling institutional interventions with regards to providing high quality and resistant seeds to farmers, and incentivizing farmers to create or join social groups in order to facilitate adaptation uptake.},
DOI = {10.3390/su11071921}
}

@article{fosu2012farmers_socioeconomic,
  title={Farmers’ perception and adaptation to climate change: a case study of Sekyedumase district in Ghana},
  author={Fosu-Mensah, Benedicta Y and Vlek, Paul LG and MacCarthy, Dilys Sefakor},
  journal={Environment, Development and Sustainability},
  volume={14},
  pages={495--505},
  year={2012},
  publisher={Springer},
  doi = {10.1007/s10668-012-9339-7}
}

@inproceedings{bittner2023lstm_downscaling,
  title={An {LSTM}-based downscaling framework for Australian precipitation projections},
  author={Bittner, Matthias and Hobeichi, Sanaa and Zawish, Muhammad and Diatta, Samo and Ozioko, Remigious and Xu, Sharon and Jantsch, Axel},
  booktitle={NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning},
  year={2023},
  URL = {https://jantsch.se/AxelJantsch/papers/2023/MatthiasBittner-CCAI-NeurIPS.pdf}
}

@article{dueben2018challenges_climate_modeling_ml,
  title={Challenges and design choices for global weather and climate models based on machine learning},
  author={Dueben, Peter D and Bauer, Peter},
  journal={Geoscientific Model Development},
  volume={11},
  number={10},
  pages={3999--4009},
  year={2018},
  publisher={Copernicus GmbH},
  doi = {10.5194/gmd-11-3999-2018}
}

@article{haggag2021deep_climate_disaster,
  title={A deep learning model for predicting climate-induced disasters},
  author={Haggag, May and Siam, Ahmad S and El-Dakhakhni, Wael and Coulibaly, Paulin and Hassini, Elkafi},
  journal={Natural Hazards},
  volume={107},
  pages={1009--1034},
  year={2021},
  publisher={Springer},
  doi = {10.1007/s11069-021-04620-0}
}

@article{keum2020real_climate_disaster_floods,
  title={Real-time flood disaster prediction system by applying machine learning technique},
  author={Keum, Ho Jun and Han, Kun Yeun and Kim, Hyun Il},
  journal={KSCE Journal of Civil Engineering},
  volume={24},
  number={9},
  pages={2835--2848},
  year={2020},
  publisher={Springer},
  doi = {10.1007/s12205-020-1677-7}
}


@inproceedings{NguyenBKGG23_foundation_climate_model,
  author       = {Tung Nguyen and
                  Johannes Brandstetter and
                  Ashish Kapoor and
                  Jayesh K. Gupta and
                  Aditya Grover},
  editor       = {Andreas Krause and
                  Emma Brunskill and
                  Kyunghyun Cho and
                  Barbara Engelhardt and
                  Sivan Sabato and
                  Jonathan Scarlett},
  title        = {ClimaX: {A} foundation model for weather and climate},
  booktitle    = {Proceedings of the International Conference on Machine Learning},
  volume       = {202},
  pages        = {25904--25938},
  year         = {2023},
  url          = {https://proceedings.mlr.press/v202/nguyen23a.html},
  timestamp    = {Mon, 28 Aug 2023 17:23:08 +0200},
  biburl       = {https://dblp.org/rec/conf/icml/NguyenBKGG23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{nguyen2021predicting_climate_finance_carbon_footprint,
  title={Predicting corporate carbon footprints for climate finance risk analyses: a machine learning approach},
  author={Nguyen, Quyen and Diaz-Rainey, Ivan and Kuruppuarachchi, Duminda},
  journal={Energy Economics},
  volume={95},
  pages={105129},
  year={2021},
  publisher={Elsevier},
  doi = {10.2139/ssrn.3617175 }
}

@article{milojevic2021machine_climate_policy,
  title={Machine learning for geographically differentiated climate change mitigation in urban areas},
  author={Milojevic-Dupont, Nikola and Creutzig, Felix},
  journal={Sustainable Cities and Society},
  volume={64},
  pages={102526},
  year={2021},
  publisher={Elsevier},
  doi = {10.1016/j.scs.2020.102526}
}

@ARTICLE{sycophancy_llms,
       author = {{Sharma}, Mrinank and {Tong}, Meg and {Korbak}, Tomasz and {Duvenaud}, David and {Askell}, Amanda and {Bowman}, Samuel R. and {Cheng}, Newton and {Durmus}, Esin and {Hatfield-Dodds}, Zac and {Johnston}, Scott R. and {Kravec}, Shauna and {Maxwell}, Timothy and {McCandlish}, Sam and {Ndousse}, Kamal and {Rausch}, Oliver and {Schiefer}, Nicholas and {Yan}, Da and {Zhang}, Miranda and {Perez}, Ethan},
        title = "{Towards Understanding Sycophancy in Language Models}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning, I.2.6},
         year = 2023,
        month = oct,
          eid = {arXiv:2310.13548},
        pages = {arXiv:2310.13548},
          doi = {10.48550/arXiv.2310.13548},
archivePrefix = {arXiv},
       eprint = {2310.13548},
 primaryClass = {cs.CL},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv231013548S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@inproceedings{sainz-etal-2023-nlp-llm-data-contamination,
    title = "{NLP} Evaluation in trouble: On the Need to Measure {LLM} Data Contamination for each Benchmark",
    author = "Sainz, Oscar  and
      Campos, Jon  and
      Garc{\'\i}a-Ferrero, Iker  and
      Etxaniz, Julen  and
      de Lacalle, Oier Lopez  and
      Agirre, Eneko",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    url = "https://aclanthology.org/2023.findings-emnlp.722",
    doi = "10.18653/v1/2023.findings-emnlp.722",
    pages = "10776--10787",
    abstract = "In this position paper we argue that the classical evaluation on Natural Language Processing (NLP) tasks using annotated benchmarks is in trouble. The worst kind of data contamination happens when a Large Language Model (LLM) is trained on the test split of a benchmark, and then evaluated in the same benchmark. The extent of the problem is unknown, as it is not straightforward to measure. Contamination causes an overestimation of the performance of a contaminated model in a target benchmark and associated task with respect to their non-contaminated counterparts. The consequences can be very harmful, with wrong scientific conclusions being published while other correct ones are discarded. This position paper defines different levels of data contamination and argues for a community effort, including the development of automatic and semi-automatic measures to detect when data from a benchmark was exposed to a model, and suggestions for flagging papers with conclusions that are compromised by data contamination.",
}

@inproceedings{li-etal-2023-halueval-llm-hallucination,
    title = "{H}alu{E}val: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models",
    author = "Li, Junyi  and
      Cheng, Xiaoxue  and
      Zhao, Xin  and
      Nie, Jian-Yun  and
      Wen, Ji-Rong",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    url = "https://aclanthology.org/2023.emnlp-main.397",
    doi = "10.18653/v1/2023.emnlp-main.397",
    pages = "6449--6464",
    abstract = "Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i.e., content that conflicts with the source or cannot be verified by the factual knowledge. To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation for Large Language Models (HaluEval) benchmark, a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, i.e., sampling-then-filtering. Besides, we also hire some human labelers to annotate the hallucinations in ChatGPT responses. The empirical results suggest that ChatGPT is likely to generate hallucinated content in specific topics by fabricating unverifiable information (i.e., about 19.5{\%} user queries). Moreover, existing LLMs face great challenges in recognizing the hallucinations in texts. While, our experiments also prove that the hallucination recognition can be improved by providing external knowledge or adding reasoning steps.",
}

@ARTICLE{mistral_7b,
       author = {{Jiang}, Albert Q. and {Sablayrolles}, Alexandre and {Mensch}, Arthur and {Bamford}, Chris and {Singh Chaplot}, Devendra and {de las Casas}, Diego and {Bressand}, Florian and {Lengyel}, Gianna and {Lample}, Guillaume and {Saulnier}, Lucile and {Renard Lavaud}, L{\'e}lio and {Lachaux}, Marie-Anne and {Stock}, Pierre and {Le Scao}, Teven and {Lavril}, Thibaut and {Wang}, Thomas and {Lacroix}, Timoth{\'e}e and {El Sayed}, William},
        title = "{Mistral 7B}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
         year = 2023,
        month = oct,
          eid = {arXiv:2310.06825},
        pages = {arXiv:2310.06825},
          doi = {10.48550/arXiv.2310.06825},
archivePrefix = {arXiv},
       eprint = {2310.06825},
 primaryClass = {cs.CL},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv231006825J},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@misc{OpenHermes2.5,
  title = {OpenHermes 2.5: An Open Dataset of Synthetic Data for Generalist LLM Assistants},
  author = {Teknium},
  year = {2023},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/teknium/OpenHermes-2.5},
  note = {Accessed on 10.04.2024}
}

https://huggingface.co/datasets/argilla/dpo-mix-7k

@misc{argilla-dpo-mix-7k,
  title = {Argilla DPO Mix: A small, high-quality DPO dataset},
  author = {Argilla},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/argilla/dpo-mix-7k},
  note = {Accessed on 10.04.2024}
}

@article{rafailov2024direct_dpo,
  title={Direct preference optimization: Your language model is secretly a reward model},
  author={Rafailov, Rafael and Sharma, Archit and Mitchell, Eric and Manning, Christopher D and Ermon, Stefano and Finn, Chelsea},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024},
  doi = {10.48550/arXiv.2305.18290}
}

@inproceedings{ippolito-etal-2019-lm-decodingcomparison,
    title = "Comparison of Diverse Decoding Methods from Conditional Language Models",
    author = "Ippolito, Daphne  and
      Kriz, Reno  and
      Sedoc, Jo{\~a}o  and
      Kustikova, Maria  and
      Callison-Burch, Chris",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'\i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    url = "https://aclanthology.org/P19-1365",
    doi = "10.18653/v1/P19-1365",
    pages = "3752--3762",
    abstract = "While conditional language models have greatly improved in their ability to output high quality natural language, many NLP applications benefit from being able to generate a diverse set of candidate sequences. Diverse decoding strategies aim to, within a given-sized candidate list, cover as much of the space of high-quality outputs as possible, leading to improvements for tasks that rerank and combine candidate outputs. Standard decoding methods, such as beam search, optimize for generating high likelihood sequences rather than diverse ones, though recent work has focused on increasing diversity in these methods. In this work, we perform an extensive survey of decoding-time strategies for generating diverse outputs from a conditional language model. In addition, we present a novel method where we over-sample candidates, then use clustering to remove similar sequences, thus achieving high diversity without sacrificing quality.",
}

@inproceedings{peng-etal-2023-towards-temperature-chatgpt,
    title = "Towards Making the Most of {C}hat{GPT} for Machine Translation",
    author = "Peng, Keqin  and
      Ding, Liang  and
      Zhong, Qihuang  and
      Shen, Li  and
      Liu, Xuebo  and
      Zhang, Min  and
      Ouyang, Yuanxin  and
      Tao, Dacheng",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    url = "https://aclanthology.org/2023.findings-emnlp.373",
    doi = "10.18653/v1/2023.findings-emnlp.373",
    pages = "5622--5633",
    abstract = "ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g, low-resource and distant-language-pairs translation. However, they usually adopt simple prompts which can not fully elicit the capability of ChatGPT. In this report, we aim to further mine ChatGPT{'}s translation ability by revisiting several aspects: temperature, task information, and domain information, and correspondingly propose two (simple but effective) prompts: Task-Specific Prompts (TSP) and Domain-Specific Prompts (DSP). We show that: 1) The performance of ChatGPT depends largely on temperature, and a lower temperature usually can achieve better performance; 2) Emphasizing the task information further improves ChatGPT{'}s performance, particularly in complex MT tasks; 3) Introducing domain information can elicit ChatGPT{'}s generalization ability and improve its performance in the specific domain; 4) ChatGPT tends to generate hallucinations for non-English-centric MT tasks, which can be partially addressed by our proposed prompts but still need to be highlighted for the MT/NLP community. We also explore the effects of advanced in-context learning strategies and find a (negative but interesting) observation: the powerful chain-of-thought prompt leads to word-by-word translation behavior, thus bringing significant translation degradation.",
}

@article{vaghefi2023chatclimate,
  title={ChatClimate: Grounding conversational {AI} in climate science},
  author={Vaghefi, Saeid Ashraf and Stammbach, Dominik and Muccione, Veruska and Bingler, Julia and Ni, Jingwei and Kraus, Mathias and Allen, Simon and Colesanti-Senni, Chiara and Wekhof, Tobias and Schimanski, Tobias and others},
  journal={Communications Earth \& Environment},
  volume={4},
  number={1},
  pages={480},
  year={2023},
  publisher={Nature Publishing Group UK London},
  doi = {10.48550/arXiv.2304.05510}
}

@inproceedings{pupneja2023understanding,
  title={Understanding Opinions Towards Climate Change on Social Media},
  author={Pupneja, Yashaswi and Zou, Yuesong and Levy, Sacha and Huang, Shenyang},
  booktitle={NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning},
  url={https://www.climatechange.ai/papers/neurips2023/27},
  year={2023}
}

@inproceedings{prasse2023towards,
  title={Towards Understanding Climate Change Perceptions: A Social Media Dataset},
  author={Prasse, Katharina and Jung, Steffen and Bravo, Isaac B and Walter, Stefanie and Keuper, Margret},
  booktitle={NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning},
  url={https://www.climatechange.ai/papers/neurips2023/3},
  year={2023}
}

@article{greenwell2023all_nlp_government_policy,
  title={Is it all talk: Do politicians that promote environmental messages on social media actually vote-in environmental policy?},
  author={Greenwell, Matthew P and Johnson, Thomas F},
  journal={Energy, Ecology and Environment},
  volume={8},
  number={1},
  pages={17--27},
  year={2023},
  publisher={Springer},
  doi = {10.1007/s40974-022-00259-0}
}

@article{toetzke2023leveraging_climate_tech_innovation,
  title={Leveraging large language models to monitor climate technology innovation},
  author={Toetzke, Malte and Probst, Benedict and Feuerriegel, Stefan},
  journal={Environmental Research Letters},
  volume={18},
  number={9},
  pages={091004},
  year={2023},
  publisher={IOP Publishing},
  doi = {10.1088/1748-9326/acf233}
}

@article{bingler2022cheap_climatebert,
  title={Cheap talk and cherry-picking: What ClimateBert has to say on corporate climate risk disclosures},
  author={Bingler, Julia Anna and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas},
  journal={Finance Research Letters},
  volume={47},
  pages={102776},
  year={2022},
  publisher={Elsevier},
  doi = {10.2139/ssrn.3796152}
}



@inproceedings{jalalzadehfard2022climedbert,
  title={CliMedBERT: A Pre-trained Language Model for Climate and Health-related Text},
  author={Jalalzadeh Fard, Babak and Hasan, Sadid A. and Bell, Jesse E.},
  booktitle={NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning},
  url={https://www.climatechange.ai/papers/neurips2022/110},
  year={2022}
}

@inproceedings{lo-etal-2020-s2orc,
    title = "{S}2{ORC}: The Semantic Scholar Open Research Corpus",
    author = "Lo, Kyle  and
      Wang, Lucy Lu  and
      Neumann, Mark  and
      Kinney, Rodney  and
      Weld, Daniel",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    url = "https://aclanthology.org/2020.acl-main.447",
    doi = "10.18653/v1/2020.acl-main.447",
    pages = "4969--4983",
    abstract = "We introduce S2ORC, a large corpus of 81.1M English-language academic papers spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts, resolved bibliographic references, as well as structured full text for 8.1M open access papers. Full text is annotated with automatically-detected inline mentions of citations, figures, and tables, each linked to their corresponding paper objects. In S2ORC, we aggregate papers from hundreds of academic publishers and digital archives into a unified source, and create the largest publicly-available collection of machine-readable academic text to date. We hope this resource will facilitate research and development of tools and tasks for text mining over academic text.",
}

@ARTICLE{climate_adaptation_frameworks_definitions,
       author = {{Armstrong}, J. and {Wilby}, R. and {Nicholls}, R.~J.},
        title = "{Climate change adaptation frameworks: an evaluation of plans for coastal Suffolk, UK}",
      journal = {Natural Hazards and Earth System Sciences},
         year = 2015,
        month = nov,
       volume = {15},
       number = {11},
        pages = {2511-2524},
          doi = {10.5194/nhess-15-2511-201510.5194/nhessd-3-4059-2015},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2015NHESS..15.2511A},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@inproceedings{jimmy_lin_bert_textbook, author = {Yates, Andrew and Nogueira, Rodrigo and Lin, Jimmy}, title = {Pretrained Transformers for Text Ranking: BERT and Beyond}, year = {2021}, isbn = {9781450382977}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3437963.3441667}, doi = {10.1145/3437963.3441667}, abstract = {The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This tutorial, based on a forthcoming book, provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has, without exaggeration, revolutionized the fields of natural language processing (NLP), information retrieval (IR), and beyond. We provide a synthesis of existing work as a single point of entry for both researchers and practitioners. Our coverage is grouped into two categories: transformer models that perform reranking in multi-stage ranking architectures and learned dense representations that perform ranking directly. Two themes pervade our treatment: techniques for handling long documents and techniques for addressing the tradeoff between effectiveness (result quality) and efficiency (query latency). Although transformer architectures and pretraining techniques are recent innovations, many aspects of their application are well understood. Nevertheless, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, we also attempt to prognosticate the future.}, booktitle = {Proceedings of the 14th ACM International Conference on Web Search and Data Mining}, pages = {1154–1156}, numpages = {3}, keywords = {text ranking, transformers}, location = {Virtual Event, Israel}, series = {WSDM '21} }

@inproceedings{visalli2023esg_enterprise,
  title={ESG Data Collection with Adaptive AI.},
  author={Visalli, Francesco and Patrizio, Antonio and Lanza, Antonio and Papaleo, Prospero and Nautiyal, Anupam and Pupo, Mariella and Scilinguo, Umberto and Oro, Ermelinda and Ruffolo, Massimo},
  booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems},
  pages={468--475},
  year={2023},
doi={10.5220/0011844500003467},
}

@article{berrang2021systematic,
  title={Systematic mapping of global research on climate and health: a machine learning review},
  author={Berrang-Ford, Lea and Sietsma, Anne J and Callaghan, Max and Minx, Jan C and Scheelbeek, Pauline FD and Haddaway, Neal R and Haines, Andy and Dangour, Alan D},
  journal={The Lancet Planetary Health},
  volume={5},
  number={8},
  pages={e514--e525},
  year={2021},
  publisher={Elsevier},
  doi = {10.1016/S2542-5196(21)00179-0}
}

@inproceedings{luo-etal-2020-detecting,
    title = "Detecting Stance in Media On Global Warming",
    author = "Luo, Yiwei  and
      Card, Dallas  and
      Jurafsky, Dan",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    url = "https://aclanthology.org/2020.findings-emnlp.296",
    doi = "10.18653/v1/2020.findings-emnlp.296",
    pages = "3296--3315",
    abstract = "Citing opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, {``}Leading scientists agree that global warming is a serious concern,{''} framing a clause which affirms their own stance ({``}that global warming is serious{''}) as an opinion endorsed (''[scientists] agree{''}) by a reputable source ({``}leading{''}). In contrast, a global warming denier might frame the same clause as the opinion of an untrustworthy source with a predicate connoting doubt: {``}Mistaken scientists claim [...].'' Our work studies opinion-framing in the global warming (GW) debate, an increasingly partisan issue that has received little attention in NLP. We introduce DeSMOG, a dataset of stance-labeled GW sentences, and train a BERT classifier to study novel aspects of argumentation in how different sides of a debate represent their own and each other{'}s opinions. From 56K news articles, we find that similar linguistic devices for self-affirming and opponent-doubting discourse are used across GW-accepting and skeptic media, though GW-skeptical media shows more opponent-doubt. We also find that authors often characterize sources as hypocritical, by ascribing opinions expressing the author{'}s own view to source entities known to publicly endorse the opposing view. We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.",
}

@inproceedings{vaid-etal-2022-towards,
    title = "Towards Fine-grained Classification of Climate Change related Social Media Text",
    author = "Vaid, Roopal  and
      Pant, Kartikey  and
      Shrivastava, Manish",
    editor = "Louvan, Samuel  and
      Madotto, Andrea  and
      Madureira, Brielen",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    url = "https://aclanthology.org/2022.acl-srw.35",
    doi = "10.18653/v1/2022.acl-srw.35",
    pages = "434--443",
    abstract = "With climate change becoming a cause of concern worldwide, it becomes essential to gauge people{'}s reactions. This can help educate and spread awareness about it and help leaders improve decision-making. This work explores the fine-grained classification and Stance detection of climate change-related social media text. Firstly, we create two datasets, ClimateStance and ClimateEng, consisting of 3777 tweets each, posted during the 2019 United Nations Framework Convention on Climate Change and comprehensively outline the dataset collection, annotation methodology, and dataset composition. Secondly, we propose the task of Climate Change stance detection based on our proposed ClimateStance dataset. Thirdly, we propose a fine-grained classification based on the ClimateEng dataset, classifying social media text into five categories: Disaster, Ocean/Water, Agriculture/Forestry, Politics, and General. We benchmark both the datasets for climate change stance detection and fine-grained classification using state-of-the-art methods in text classification. We also create a Reddit-based dataset for both the tasks, ClimateReddit, consisting of 6262 pseudo-labeled comments along with 329 manually annotated comments for the label. We then perform semi-supervised experiments for both the tasks and benchmark their results using the best-performing model for the supervised experiments. Lastly, we provide insights into the ClimateStance and ClimateReddit using part-of-speech tagging and named-entity recognition.",
}



@inproceedings{leippold2020climatefever,
  title={Climate-FEVER: A Dataset for Verification of Real-World Climate Claims},
  author={Leippold, Markus and Diggelmann, Thomas},
  booktitle={NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning},
  url={https://www.climatechange.ai/papers/neurips2020/67},
  year={2020}
}

@inproceedings{hershcovich-etal-2022-towards,
    title = "Towards Climate Awareness in {NLP} Research",
    author = "Hershcovich, Daniel  and
      Webersinke, Nicolas  and
      Kraus, Mathias  and
      Bingler, Julia  and
      Leippold, Markus",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    url = "https://aclanthology.org/2022.emnlp-main.159",
    doi = "10.18653/v1/2022.emnlp-main.159",
    pages = "2480--2494",
    abstract = "The climate impact of AI, and NLP research in particular, has become a serious issue given the enormous amount of energy that is increasingly being used for training and running computational models. Consequently, increasing focus is placed on efficient NLP. However, this important initiative lacks simple guidelines that would allow for systematic climate reporting of NLP research. We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact, and present a quantitative survey to demonstrate this. As a remedy, we propose a climate performance model card with the primary purpose of being practically usable with only limited information about experiments and the underlying computer hardware. We describe why this step is essential to increase awareness about the environmental impact of NLP research and, thereby, paving the way for more thorough discussions.",
}

@inproceedings{strubell-etal-2019-energy,
    title = "Energy and Policy Considerations for Deep Learning in {NLP}",
    author = "Strubell, Emma  and
      Ganesh, Ananya  and
      McCallum, Andrew",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'\i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    url = "https://aclanthology.org/P19-1355",
    doi = "10.18653/v1/P19-1355",
    pages = "3645--3650",
    abstract = "Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP. Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.",
}

@ARTICLE{climabench,
       author = {{Spokoyny}, Daniel and {Laud}, Tanmay and {Corringham}, Tom and {Berg-Kirkpatrick}, Taylor},
        title = "{Towards Answering Climate Questionnaires from Unstructured Climate Reports}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, Computer Science - Machine Learning},
         year = 2023,
        month = jan,
          eid = {arXiv:2301.04253},
        pages = {arXiv:2301.04253},
          doi = {10.48550/arXiv.2301.04253},
archivePrefix = {arXiv},
       eprint = {2301.04253},
 primaryClass = {cs.CL},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230104253S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@inproceedings{wolf-etal-2020-transformers,
    title = "Transformers: State-of-the-Art Natural Language Processing",
    author = "Wolf, Thomas  and
      Debut, Lysandre  and
      Sanh, Victor  and
      Chaumond, Julien  and
      Delangue, Clement  and
      Moi, Anthony  and
      Cistac, Pierric  and
      Rault, Tim  and
      Louf, Remi  and
      Funtowicz, Morgan  and
      Davison, Joe  and
      Shleifer, Sam  and
      von Platen, Patrick  and
      Ma, Clara  and
      Jernite, Yacine  and
      Plu, Julien  and
      Xu, Canwen  and
      Le Scao, Teven  and
      Gugger, Sylvain  and
      Drame, Mariama  and
      Lhoest, Quentin  and
      Rush, Alexander",
    editor = "Liu, Qun  and
      Schlangen, David",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    url = "https://aclanthology.org/2020.emnlp-demos.6",
    doi = "10.18653/v1/2020.emnlp-demos.6",
    pages = "38--45",
    abstract = "Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at \url{https://github.com/huggingface/transformers}.",
}

@Article{Cowls2023,
author={Cowls, Josh
and Tsamados, Andreas
and Taddeo, Mariarosaria
and Floridi, Luciano},
title={The AI gambit: leveraging artificial intelligence to combat climate change---opportunities, challenges, and recommendations},
journal={AI{\&} Society},
year={2023},
month={Feb},
day={01},
volume={38},
number={1},
pages={283-307},
abstract={In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI's greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment.},
issn={1435-5655},
doi={10.1007/s00146-021-01294-x},
url={https://doi.org/10.1007/s00146-021-01294-x}
}

@inproceedings{gururangan-etal-2020-dont-dapt,
    title = "Don{'}t Stop Pretraining: Adapt Language Models to Domains and Tasks",
    author = "Gururangan, Suchin  and
      Marasovi{\'c}, Ana  and
      Swayamdipta, Swabha  and
      Lo, Kyle  and
      Beltagy, Iz  and
      Downey, Doug  and
      Smith, Noah A.",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    url = "https://aclanthology.org/2020.acl-main.740",
    doi = "10.18653/v1/2020.acl-main.740",
    pages = "8342--8360",
    abstract = "Language models pretrained on text from a wide variety of sources form the foundation of today{'}s NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains, under both high- and low-resource settings. Moreover, adapting to the task{'}s unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining. Finally, we show that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable. Overall, we consistently find that multi-phase adaptive pretraining offers large gains in task performance.",
}

@inproceedings{gao-etal-2021-coil,
    title = "{COIL}: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List",
    author = "Gao, Luyu  and
      Dai, Zhuyun  and
      Callan, Jamie",
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    url = "https://aclanthology.org/2021.naacl-main.241",
    doi = "10.18653/v1/2021.naacl-main.241",
    pages = "3030--3042",
    abstract = "Classical information retrieval systems such as BM25 rely on exact lexical match and can carry out search efficiently with inverted list index. Recent neural IR models shifts towards soft matching all query document terms, but they lose the computation efficiency of exact match systems. This paper presents COIL, a contextualized exact match retrieval architecture, where scoring is based on overlapping query document tokens{'} contextualized representations. The new architecture stores contextualized token representations in inverted lists, bringing together the efficiency of exact match and the representation power of deep language models. Our experimental results show COIL outperforms classical lexical retrievers and state-of-the-art deep LM retrievers with similar or smaller latency.",
}

@article{shi2023generalist,
  title={Generalist vision foundation models for medical imaging: A case study of segment anything model on zero-shot medical segmentation},
  author={Shi, Peilun and Qiu, Jianing and Abaxi, Sai Mu Dalike and Wei, Hao and Lo, Frank P-W and Yuan, Wu},
  journal={Diagnostics},
  volume={13},
  number={11},
  pages={1947},
  year={2023},
  publisher={MDPI},
  doi = {10.3390/diagnostics13111947},
}

@article{kendall1938tau,
  title={A new measure of rank correlation},
  author={Kendall, Maurice G},
  journal={Biometrika},
  volume={30},
  number={1/2},
  pages={81--93},
  year={1938},
  publisher={JSTOR},
  doi = {10.1093/biomet/30.1-2.81}
}

@ARTICLE{buffer_overflow_moe,
       author = {{Hayes}, Jamie and {Shumailov}, Ilia and {Yona}, Itay},
        title = "{Buffer Overflow in Mixture of Experts}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Cryptography and Security, Computer Science - Machine Learning},
         year = 2024,
        month = feb,
          eid = {arXiv:2402.05526},
        pages = {arXiv:2402.05526},
          doi = {10.48550/arXiv.2402.05526},
archivePrefix = {arXiv},
       eprint = {2402.05526},
 primaryClass = {cs.CR},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240205526H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{webb2023climate_csa,
  title={Climate services for agriculture: Tools for informing decisions relating to climate change and climate variability in the Wine industry},
  author={Webb, Leanne and Tozer, Carly and Bettio, Lynette and Darbyshire, Rebecca and Robinson, Bella and Fleming, Aysha and Tijs, Sigrid and Bodman, Roger and Prakash, Mahesh and others},
  journal={Australian Journal of Grape and Wine Research},
  volume={2023},
  year={2023},
  publisher={Hindawi},
  doi = {10.1155/2023/5025359}
}

@article{bodansky1993united,
  title={The United Nations framework convention on climate change: a commentary},
  author={Bodansky, Daniel},
  journal={Yale Journal of International Law},
  volume={18},
  pages={451},
  year={1993},
  publisher={HeinOnline},
  doi = {20.500.13051/6301}
}

@article{massetti2018measuring_climate_adaptation,
  title={Measuring climate adaptation: Methods and evidence},
  author={Massetti, Emanuele and Mendelsohn, Robert},
  journal={Review of Environmental Economics and Policy},
  year={2018},
  publisher={The University of Chicago Press},
  doi = {10.1093/reep/rey007}
}

@article{cradock2020climate_adaptation_farmer,
  title={Climate adaptation pathways for agriculture: Insights from a participatory process},
  author={Cradock-Henry, Nicholas A and Blackett, Paula and Hall, Madeline and Johnstone, Paul and Teixeira, Edmar and Wreford, Anita},
  journal={Environmental Science \& Policy},
  volume={107},
  pages={66--79},
  year={2020},
  publisher={Elsevier},
  doi = {10.1016/j.envsci.2020.02.020}
}

@article{smithers2001technology_climate_adaptation,
  title={Technology innovation as a strategy for climate adaptation in agriculture},
  author={Smithers, John and Blay-Palmer, Alison},
  journal={Applied Geography},
  volume={21},
  number={2},
  pages={175--197},
  year={2001},
  publisher={Elsevier},
  doi = {10.1016/S0143-6228(01)00004-2}
}

@article{de2022adapting_weather_climate_adaptation,
  title={Adapting to climate change through anticipatory action: The potential use of weather-based early warnings},
  author={de Perez, Erin Coughlan and Harrison, Laura and Berse, Kristoffer and Easton-Calabria, Evan and Marunye, Joalane and Marake, Makoala and Murshed, Sonia Binte and Zauisomue, Erlich-Honest and others},
  journal={Weather and Climate Extremes},
  volume={38},
  pages={100508},
  year={2022},
  publisher={Elsevier},
  doi = {10.1016/j.wace.2022.100508}
}

@article{lenzholzer2020awareness_climate_adaptation_behaviour,
  title={Awareness of urban climate adaptation strategies--an international overview},
  author={Lenzholzer, Sanda and Carsjens, Gerrit-Jan and Brown, Robert D and Tavares, Silvia and Vanos, Jennifer and Kim, YouJoung and Lee, Kanghyun},
  journal={Urban climate},
  volume={34},
  pages={100705},
  year={2020},
  publisher={Elsevier},
  doi = {10.1016/j.uclim.2020.100705}
}

@article{van2011representative_RCP,
  title={The representative concentration pathways: an overview},
  author={Van Vuuren, Detlef P and Edmonds, Jae and Kainuma, Mikiko and Riahi, Keywan and Thomson, Allison and Hibbard, Kathy and Hurtt, George C and Kram, Tom and Krey, Volker and Lamarque, Jean-Francois and others},
  journal={Climatic change},
  volume={109},
  pages={5--31},
  year={2011},
  publisher={Springer},
  doi = {10.1007/s10584-011-0148-z}
}

@ARTICLE{rag_survey,
       author = {{Gao}, Yunfan and {Xiong}, Yun and {Gao}, Xinyu and {Jia}, Kangxiang and {Pan}, Jinliu and {Bi}, Yuxi and {Dai}, Yi and {Sun}, Jiawei and {Wang}, Meng and {Wang}, Haofen},
        title = "{Retrieval-Augmented Generation for Large Language Models: A Survey}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence},
         year = 2023,
        month = dec,
          eid = {arXiv:2312.10997},
        pages = {arXiv:2312.10997},
          doi = {10.48550/arXiv.2312.10997},
archivePrefix = {arXiv},
       eprint = {2312.10997},
 primaryClass = {cs.CL},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv231210997G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{lmsys,
       author = {{Chiang}, Wei-Lin and {Zheng}, Lianmin and {Sheng}, Ying and {Nikolas Angelopoulos}, Anastasios and {Li}, Tianle and {Li}, Dacheng and {Zhang}, Hao and {Zhu}, Banghua and {Jordan}, Michael and {Gonzalez}, Joseph E. and {Stoica}, Ion},
        title = "{Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
         year = 2024,
        month = mar,
          eid = {arXiv:2403.04132},
        pages = {arXiv:2403.04132},
          doi = {10.48550/arXiv.2403.04132},
archivePrefix = {arXiv},
       eprint = {2403.04132},
 primaryClass = {cs.AI},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240304132C},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{llm_survey_paper,
       author = {{Zhao}, Wayne Xin and {Zhou}, Kun and {Li}, Junyi and {Tang}, Tianyi and {Wang}, Xiaolei and {Hou}, Yupeng and {Min}, Yingqian and {Zhang}, Beichen and {Zhang}, Junjie and {Dong}, Zican and {Du}, Yifan and {Yang}, Chen and {Chen}, Yushuo and {Chen}, Zhipeng and {Jiang}, Jinhao and {Ren}, Ruiyang and {Li}, Yifan and {Tang}, Xinyu and {Liu}, Zikang and {Liu}, Peiyu and {Nie}, Jian-Yun and {Wen}, Ji-Rong},
        title = "{A Survey of Large Language Models}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence},
         year = 2023,
        month = mar,
          eid = {arXiv:2303.18223},
        pages = {arXiv:2303.18223},
          doi = {10.48550/arXiv.2303.18223},
archivePrefix = {arXiv},
       eprint = {2303.18223},
 primaryClass = {cs.CL},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230318223Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@inproceedings{c1e2faff_chincilla_scaling,
 author = {Hoffmann, Jordan and Borgeaud, Sebastian and Mensch, Arthur and Buchatskaya, Elena and Cai, Trevor and Rutherford, Eliza and de Las Casas, Diego and Hendricks, Lisa Anne and Welbl, Johannes and Clark, Aidan and Hennigan, Thomas and Noland, Eric and Millican, Katherine and van den Driessche, George and Damoc, Bogdan and Guy, Aurelia and Osindero, Simon and Simonyan, Kar\'{e}n and Elsen, Erich and Vinyals, Oriol and Rae, Jack and Sifre, Laurent},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
 pages = {30016--30030},
 title = {An empirical analysis of compute-optimal large language model training},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/c1e2faff6f588870935f114ebe04a3e5-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}

@inproceedings{caballeroGRK23_broken_scaling_law,
  author       = {Ethan Caballero and
                  Kshitij Gupta and
                  Irina Rish and
                  David Krueger},
  title        = {Broken Neural Scaling Laws},
  booktitle    = {The Eleventh International Conference on Learning Representations,
                  {ICLR} 2023, Kigali, Rwanda, May 1-5, 2023},
  year         = {2023},
  url          = {https://openreview.net/pdf?id=sckjveqlCZ},
  timestamp    = {Fri, 30 Jun 2023 14:55:53 +0200},
  biburl       = {https://dblp.org/rec/conf/iclr/CaballeroGRK23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{control_vectors,
       author = {{Zou}, Andy and {Phan}, Long and {Chen}, Sarah and {Campbell}, James and {Guo}, Phillip and {Ren}, Richard and {Pan}, Alexander and {Yin}, Xuwang and {Mazeika}, Mantas and {Dombrowski}, Ann-Kathrin and {Goel}, Shashwat and {Li}, Nathaniel and {Byun}, Michael J. and {Wang}, Zifan and {Mallen}, Alex and {Basart}, Steven and {Koyejo}, Sanmi and {Song}, Dawn and {Fredrikson}, Matt and {Zico Kolter}, J. and {Hendrycks}, Dan},
        title = "{Representation Engineering: A Top-Down Approach to AI Transparency}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computers and Society},
         year = 2023,
        month = oct,
          eid = {arXiv:2310.01405},
        pages = {arXiv:2310.01405},
          doi = {10.48550/arXiv.2310.01405},
archivePrefix = {arXiv},
       eprint = {2310.01405},
 primaryClass = {cs.LG},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv231001405Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{rillig2023risks_llm_climate,
  title={Risks and benefits of large language models for the environment},
  author={Rillig, Matthias C and {\AA}gerstrand, Marlene and Bi, Mohan and Gould, Kenneth A and Sauerland, Uli},
  journal={Environmental Science \& Technology},
  volume={57},
  number={9},
  pages={3464--3466},
  year={2023},
  publisher={ACS Publications},
  doi = {10.1021/acs.est.3c01106}
}

@misc{label_studio,
  title={{Label Studio}: Data labeling software},
  url={https://github.com/heartexlabs/label-studio},
  note={Last accessed: 15.05.2024},
  author={
    Maxim Tkachenko and
    Mikhail Malyuk and
    Andrey Holmanyuk and
    Nikolai Liubimov},
  year={2020-2022},
} 
