@inproceedings{wang-sun-2022-trial2vec,
    title = "{T}rial2{V}ec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision",
    author = "Wang, Zifeng  and
      Sun, Jimeng",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    url = "https://aclanthology.org/2022.findings-emnlp.476",
    pages = "6377--6390",
}


@inproceedings{litschko-etal-2022-parameter,
    title = "Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval",
    author = "Litschko, Robert  and Vuli{\'c}, Ivan  and Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    url = "https://aclanthology.org/2022.coling-1.90",
    pages = "1071--1082",
}


@inproceedings{TREC:Overview:2021,
    author  = {Ian Soboroff},
    title   = {Overview of TREC 2021},
	booktitle={TREC},
 	year = {2021},
}

@inproceedings{Rybinski:Nguyen:Karimi:2021,
	booktitle={TREC},
	title = {{CSIROmed} Team Report of {TREC} 2021 Clinical Trials track:
Experiments with {BERT} Reranking Methods},
	author = {Maciej Rybinski and Vincent Nguyen and Sarvnaz Karimi},
	year = {2021},
	url = {https://trec.nist.gov/pubs/trec30/papers/CSIROmed-CT.pdf},
}


@inproceedings{zhou2016text,
  title={Text classification improved by integrating bidirectional {LSTM} with two-dimensional max pooling},
  author={Zhou, Peng and Qi, Zhenyu and Zheng, Suncong and Xu, Jiaming and Bao, Hongyun and Xu, Bo},
  booktitle={The 26th International Conference on Computational Linguistics},
  year={2016},
  pages = {3485–3495},
}


@inproceedings{Mitra_Counts_Pennebaker_2021, 
    title={Understanding Anti-Vaccination Attitudes in Social Media},  url={https://ojs.aaai.org/index.php/ICWSM/article/view/14729}, 
    booktitle={Proceedings of the International AAAI Conference on Web and Social Media}, 
    author={Mitra, Tanushree and Counts, Scott and Pennebaker, James}, 
    year={2021}, 
    pages={269-278} 
    }

@article{Green:Craig:2022,
    author  = {Lanyi, Katherine and Green, Rhiannon and Craig, Dawn and Marshall, Christopher},
    year    = {2022},
    title   = "COVID-19 Vaccine Hesitancy: Analysing Twitter to Identify Barriers to Vaccination in a Low Uptake Region of the UK",
    journal = {Frontiers in digital health},
    volume  = {3}, 
    doi     = {10.3389/fdgth.2021.804855},
}

@InProceedings{CrossNet,
author="Zheng, Haitian
and Ji, Mengqi
and Wang, Haoqian
and Liu, Yebin
and Fang, Lu",
editor="Ferrari, Vittorio
and Hebert, Martial
and Sminchisescu, Cristian
and Weiss, Yair",
title="CrossNet: An End-to-End Reference-Based Super Resolution Network Using Cross-Scale Warping",
booktitle="European Conference on Computer Vision",
year="2018",
pages="87--104",

}

@inproceedings{augenstein-etal-2016-stance,
    title = "Stance Detection with Bidirectional Conditional Encoding",
    author = {Augenstein, Isabelle  and
      Rockt{\"a}schel, Tim  and
      Vlachos, Andreas  and
      Bontcheva, Kalina},
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    
    url = "https://aclanthology.org/D16-1084",
    doi = "10.18653/v1/D16-1084",
    pages = "876--885",
}

@article{Medford:Saleh:2020,
    author = {Medford, R. J.and  Saleh, S. N. and Sumarsono, A. and Perl, T. M. and Lehmann, C. U.},
    year    = {2020},
    title   = {An {I}nfodemic: Leveraging High-Volume {T}witter Data to Understand Early Public Sentiment for the {C}oronavirus Disease 2019 Outbreak},
    journal = {Open forum infectious diseases},
    volume = {7},
    number  = {7}, 
    doi     = {10.1093/ofid/ofaa258},
}

@article{Saif:Sobhani:2017,
author = {Mohammad, Saif M. and Sobhani, Parinaz and Kiritchenko, Svetlana},
title = {Stance and Sentiment in Tweets},
year = {2017},
volume = {17},
number = {3},
doi = {10.1145/3003433},
abstract = {We can often detect from a person’s utterances whether he or she is in favor of or against a given target entity—one’s stance toward the target. However, a person may express the same stance toward a target by using negative or positive language. Here for the first time we present a dataset of tweet–target pairs annotated for both stance and sentiment. The targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. Partitions of this dataset were used as training and test sets in a SemEval-2016 shared task competition. We propose a simple stance detection system that outperforms submissions from all 19 teams that participated in the shared task. Additionally, access to both stance and sentiment annotations allows us to explore several research questions. We show that although knowing the sentiment expressed by a tweet is beneficial for stance classification, it alone is not sufficient. Finally, we use additional unlabeled data through distant supervision techniques and word embeddings to further improve stance classification.},
journal = {ACM Transactions on Internet Technology},
pages = {1--23},
keywords = {polarity, tweets, opinion, text classification, Stance, sentiment}
}

  



@inproceedings{Ofoghi:Mann:Verspoor:2016,
    author  = {Ofoghi, Bahadorreza, and Mann, Meghan and Karin Verspoor},
    title   = {Towards early discovery of salient health threats: a social media emotion classification technique},
    booktitle = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing}, 
    year    = {2016},
    pages   = {504--515},
    doi     = {10.1142/9789814749411_0046},
}

@inproceedings{conforti-etal-2020-will,
    title = "Will-They-Won{'}t-They: A Very Large Dataset for Stance Detection on {T}witter",
    author = "Conforti, Costanza  and
      Berndt, Jakob  and
      Pilehvar, Mohammad Taher  and
      Giannitsarou, Chryssi  and
      Toxvaerd, Flavio  and
      Collier, Nigel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    year = "2020",
    doi = "10.18653/v1/2020.acl-main.157",
    pages = "1715--1724",
}


@inproceedings{conforti-etal-2021-synthetic,
    title = "Synthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a {T}witter corpus.",
    author = "Conforti, Costanza  and
      Berndt, Jakob  and
      Pilehvar, Mohammad Taher  and
      Giannitsarou, Chryssi  and
      Toxvaerd, Flavio  and
      Collier, Nigel",
    booktitle = "Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    year = "2021",
    url = "https://aclanthology.org/2021.wassa-1.19",
    pages = "181--187",
    abstract = "Cross-target generalization is a known problem in stance detection (SD), where systems tend to perform poorly when exposed to targets unseen during training. Given that data annotation is expensive and time-consuming, finding ways to leverage abundant unlabeled in-domain data can offer great benefits. In this paper, we apply a weakly supervised framework to enhance cross-target generalization through synthetically annotated data. We focus on Twitter SD and show experimentally that integrating synthetic data is helpful for cross-target generalization, leading to significant improvements in performance, with gains in F1 scores ranging from +3.4 to +5.1.",
}



@inproceedings{morante-etal-2020-annotating,
    title = "Annotating Perspectives on Vaccination",
    author = "Morante, Roser  and
      van Son, Chantal  and
      Maks, Isa  and
      Vossen, Piek",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    year = "2020",
    address = "Marseille, France",
    url = "https://aclanthology.org/2020.lrec-1.611",
    pages = "4964--4973",
    abstract = "In this paper we present the Vaccination Corpus, a corpus of texts related to the online vaccination debate that has been annotated with three layers of information about perspectives: attribution, claims and opinions. Additionally, events related to the vaccination debate are also annotated. The corpus contains 294 documents from the Internet which reflect different views on vaccinations. It has been compiled to study the language of online debates, with the final goal of experimenting with methodologies to extract and contrast perspectives in the framework of the vaccination debate.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}

@inproceedings{skeppstedt-etal-2017-automatic,
    title = "Automatic detection of stance towards vaccination in online discussion forums",
    author = "Skeppstedt, Maria  and
      Kerren, Andreas  and
      Stede, Manfred",
    booktitle = "Proceedings of the International Workshop on Digital Disease Detection using Social Media",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    url = "https://aclanthology.org/W17-5801",
    pages = "1--8",
}

@inproceedings{jimeno-yepes-etal-2015-investigating,
    title = "Investigating Public Health Surveillance using {T}witter",
    author = "Jimeno Yepes, Antonio  and
      MacKinlay, Andrew  and
      Han, Bo",
    booktitle = "Proceedings of {B}io{NLP} 15",
    year = "2015",
    address = "Beijing, China",
    doi = "10.18653/v1/W15-3821",
    pages = "164--170",
}


@inproceedings{huang-etal-2016-syndromic,
    title = "Syndromic Surveillance using Generic Medical Entities on {T}witter",
    author = "Huang, Pin  and
      MacKinlay, Andrew  and
      Yepes, Antonio Jimeno",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop",
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://aclanthology.org/U16-1004",
    pages = "35--44",
}


@inproceedings{xu-etal-2018-cross,
    title = "Cross-Target Stance Classification with Self-Attention Networks",
    author = "Xu, Chang  and
      Paris, C{\'e}cile  and
      Nepal, Surya  and
      Sparks, Ross",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics",
    year = "2018",
    doi = "10.18653/v1/P18-2123",
    pages = "778--783",
}

@article{Joshi:Karimi:2020,
    title   = {Automated monitoring of tweets for early detection of the 2014 {Ebola} epidemic},
    author  = {Aditya Joshi and Ross Sparks and Sarvnaz Karimi and Sheng-Lun Jason Yan and Abrar Ahmad Chughtai and Cecile Paris and C. Raina MacIntyre}, 
    journal = {PLOS One},
    year    = {2020},
}


@article{Joshi:Karimi:2019,
author = {Joshi, Aditya and Karimi, Sarvnaz and Sparks, Ross and Paris, C\'{e}cile and Macintyre, C. Raina},
title = {Survey of Text-Based Epidemic Intelligence: A Computational Linguistics Perspective},
year = {2019},
address = {New York, NY},
volume = {52},
number = {6},
articleno = {119},
numpages = {19},
keywords = {Epidemic intelligence, natural language processing}
}


@article{calvo2017natural,
  title={Natural language processing in mental health applications using non-clinical texts},
  author={Calvo, Rafael A and Milne, David N and Hussain, M Sazzad and Christensen, Helen},
  journal={Natural Language Engineering},
  volume={23},
  number={5},
  pages={649--685},
  year={2017},
}


@article{Joshi:Sparks:McHugh:2020,
    author = {Aditya Joshi and Ross Sparks and James McHugh and Sarvnaz Karimi and Cecile Paris and C. Raina MacIntyre},
    title  = {Harnessing Tweets for Early Detection of an Acute Disease Event},
    journal = {Epidemiology},
    year   = {2020},
    volume  = {31},
    number  = {1},
    pages   = {90--97}
}

@inproceedings{joshi-etal-2018-shot,
    title = "Shot Or Not: Comparison of {NLP} Approaches for Vaccination Behaviour Detection",
    author = "Joshi, Aditya  and
      Dai, Xiang  and
      Karimi, Sarvnaz  and
      Sparks, Ross  and
      Paris, C{\'e}cile  and
      MacIntyre, C Raina",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    year = "2018",
    address = "Brussels, Belgium",
    
    doi = "10.18653/v1/W18-5911",
    pages = "43--47",
}

@inproceedings{iyer-etal-2019-figurative,
    title = "Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection",
    author = "Iyer, Adith  and
      Joshi, Aditya  and
      Karimi, Sarvnaz  and
      Sparks, Ross  and
      Paris, Cecile",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    
    url = "https://aclanthology.org/P19-1108",
    doi = "10.18653/v1/P19-1108",
    pages = "1142--1147",
    abstract = "Personal health mention detection deals with predicting whether or not a given sentence is a report of a health condition. Past work mentions errors in this prediction when symptom words, i.e., names of symptoms of interest, are used in a figurative sense. Therefore, we combine a state-of-the-art figurative usage detection with CNN-based personal health mention detection. To do so, we present two methods: a pipeline-based approach and a feature augmentation-based approach. The introduction of figurative usage detection results in an average improvement of 2.21{\%} F-score of personal health mention detection, in the case of the feature augmentation-based approach. This paper demonstrates the promise of using figurative usage detection to improve personal health mention detection.",
}


@article{Conway:Hu:Chapman:2019,
    author = {Mike Conway and Mengke Hu and Wendy W. Chapman},
    title  = {Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and Consumer Generated Data},
    year    = {2019},
    volume  = {28},
    number  = {1},
    pages   = {208--217},
    doi = {10.1055/s-0039-1677918},
}

@article{paul2017social,
  title={Social monitoring for public health},
  author={Paul, Michael J and Dredze, Mark},
  journal={Synthesis Lectures on Information Concepts, Retrieval, and Services},
  volume={9},
  number={5},
  pages={1--183},
  year={2017},
  publisher={Morgan \& Claypool Publishers}
}


@InProceedings{Qu:Arguello:Wang:2021,
author="Qu, Jiaming
and Arguello, Jaime
and Wang, Yue",
title="A Deep Analysis of an Explainable Retrieval Model for Precision Medicine Literature Search",
booktitle="Advances in  Information Retrieval",
year="2021",
pages="544--557",
}



@inproceedings{Qu:Arguello:Wang:20220,
    author = {Qu, Jiaming and Arguello, Jaime and Wang, Yue},
    title = {Towards Explainable Retrieval Models for Precision Medicine Literature Search},
    year = {2020},
    url = {https://doi.org/10.1145/3397271.3401277},
    doi = {10.1145/3397271.3401277},
    booktitle = {SIGIR},
    pages = {1593–1596},
    keywords = {medical information retrieval, explainable retrieval model, structured relevance feedback},
}

@inproceedings{Rybinski:Watts:Karimi:2022,
    author = {Rybinski, Maciej and Watts, Liam and Karimi, Sarvnaz},
    title  = {{A2A-API}: A Prototype for Biomedical Information Retrieval Research and Benchmarking},
    year = {2022},
    url = {https://doi.org/10.1145/3477495.3531667},
    doi = {10.1145/3477495.3531667},
    booktitle = {SIGIR},
    pages = {3318--3322},
}


@inproceedings{Turian:2010,
    author = {Joseph Turian, Lev Ratinov, and Yoshua Bengio},
    year   = {2010},
    title  = {Word representations: a simple and general method
for semi-supervised learning},
    booktitle   = {The 48th annual meeting of the association for computational linguistics},
    pages   = {384–394},
}

@inproceedings{MacAvaney:Yates:2021,
    author = {MacAvaney, Sean and Yates, Andrew and Feldman, Sergey and Downey, Doug and Cohan, Arman and Goharian, Nazli},
    title = {Simplified Data Wrangling with ir\_datasets},
    year = {2021},
    booktitle = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
    pages = {2429--2436},
}


@inproceedings{pyserini,
author = {Lin, Jimmy and Ma, Xueguang and Lin, Sheng-Chieh and Yang, Jheng-Hong and Pradeep, Ronak and Nogueira, Rodrigo},
title = {Pyserini: {A P}ython Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations},
year = {2021},
booktitle = {SIGIR},
pages = {2356–-2362}
}


@article{A2A:BMC,
    author  = {Rybinski, Maciej and Karimi, Sarvnaz and Nguyen, Vincent and Paris, Cecile},
    title   = {{A2A: A} platform for research in biomedical literature search},
    journal = {BMC Bioinformatics},
    volume  = {21}, 
    number  = {572},     
    year = {2020}, 
}

@inproceedings{cikm_sorafenib,
  title={{W}ill {S}orafenib help? {T}reatment-aware {R}eranking in {P}recision {M}edicine {S}earch},
  author={Rybinski, Maciej and Karimi, Sarvnaz},
  booktitle={{P}roceedings of the 30th {I}nternational {ACM} {C}onference on {I}nformation and {K}nowledge {M}anagement ({CIKM})},
  year={2021}
}

@inproceedings{Cer:Yang:EMNLP:2018,
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 and Brian Strope and Ray Kurzweil},
title = {Universal Sentence Encoder for English},    
booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (System Demonstrations)},    
address = {Brussels, Belgium},   
pages = {169--174},    
year = {2018}
}

@inproceedings{Wang:Liu:ACL:2014,    
author = {Yue Wang and Xitong Liu and Hui Fang},    
title = {A study of concept-based weighting regularization for medical records search},    
booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics},
address = {Baltimore, Maryland},    
pages = {603--612},    
year = {2014}
}

@article{Choi:Choi:JBI:2014,    
author = {Sungbin Choi and Jinwook Choi and Sooyoung Yoo and Heechun Kim and Youngho Lee},    
title = {Semantic concept-enriched dependence model for medical information retrieval},    
journal = {Journal of biomedical informatics},    
pages = {18-27},    
year = {2014}
}

@article{Stubbs:Filannino:JMIA:2019,    
author = {Amber Stubbs and Michele Filannino and Ergin Soysal and Samuel Henry and Özlem Uzuner},    
title = {Cohort selection for clinical trials: {N2C2} 2018 shared task track 1},    
journal = {Journal of the American Medical Informatics Association},    
pages = {1163--1171},    
year = {2019}
}

@article{Oleynik:Kugic:JMIA:2019,    
author = {Michel Oleynik and Amila Kugic and Zdenko Kasac and Markus Kreuzthaler},    
title = {Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification},    
journal = {Journal of the American Medical Informatics Association},    
pages = {1247--1254},    
year = {2019}
}

@inproceedings{Antunes:Silva:HEALTHINF:2019,    
author = {Rui Antunes and João Figueira Silva and Arnaldo Pereira and Sérgio Matos},    
title = {Rule-based and Machine Learning Hybrid System for Patient Cohort Selection},    
booktitle = {International Conference on Health Informatics},    
address = {Prague, Czech Republic},    
pages = {59--67},    
year = {2019}
}

@article{Alafaireet:2017,
    title   = {Embedding a Medical Search Engine Within an Electronic Health Record},
    author  = {Alafaireet, P., Belden, J., Botkin, M., Kochendorfer, K., Kruse, R., Strecker, D., and Williams, J.},
    journal = {Missouri medicine},
    volume  = {114},
    number  = {4},
    year    = {2017},
}

@inproceedings{koopman2016test,
  title={A test collection for matching patients to clinical trials},
  author={Koopman, Bevan and Zuccon, Guido},
  booktitle={Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval},
  pages={669--672},
  year={2016}
}

@article{Kakumanu:Manns:2019,
    title   = {Cost analysis and efficacy of recruitment strategies used in a large pragmatic community-based clinical trial targeting low-income seniors: a comparative descriptive analysis},
    author  = {Sravya Kakumanu and Braden Manns and  Sophia Tran and Terry Saunders-Smith and Brenda Hemmelgarn and Marcello Tonelli and Ross Tsuyuki and  Noah Ivers and  Danielle Southern and Jeff Bakal and David Campbell},
    journal = {Trials},
    volume  = {20},
    number  = {577},
    year    = {2019},
}

@inproceedings{Jiang:Chunhua:2014,
    author  = {Jiang, Silis Y, and Chunhua Weng},
    title   = "{Cross-system evaluation of clinical trial search engines}",
    booktitle  = {AMIA Joint Summits on Translational Science proceedings},
    pages   = {223--229},
    year    = {2014},
}


@inproceedings{plaisant2008searching,
  title={Searching electronic health records for temporal patterns in patient histories: A case study with microsoft amalga},
  author={Plaisant, Catherine and Lam, Stanley and Shneiderman, Ben and Smith, Mark S and Roseman, David and Marchand, Greg and Gillam, Michael and Feied, Craig and Handler, Jonathan and Rappaport, Hank},
  booktitle={AMIA annual symposium proceedings},
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  pages={601},
  year={2008},
}

@misc{medgen,
	author={{NCBI}},
	title={{MedGen}},
	howpublished = {\url{https://www.ncbi.nlm.nih.gov/medgen/}},
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	note="[Online; accessed 10-Apr-2021]"
}

@misc{ctgov,
	author={NLM},
	title={ClinicalTrials.gov},
	howpublished = {\url{https://clinicaltrials.gov/}},
	year={2021},
	note="[Online; accessed 08-Mar-2021]"
}

@misc{cdsr,
	author={Cochrane Library},
	title={Cochrane Database of Systematic Reviews},
	howpublished = {\url{https://www.cochranelibrary.com/cdsr/reviews}},
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	note="[Online; accessed 26-Feb-2021]"
}

@article{wu2017semehr,
  title={SemEHR: surfacing semantic data from clinical notes in electronic health records for tailored care, trial recruitment, and clinical research},
  author={Wu, Honghan and Toti, Giulia and Morley, Katherine I and Ibrahim, Zina and Folarin, Amos and Kartoglu, Ismail and Jackson, Richard and Agrawal, Asha and Stringer, Clive and Gale, Darren and others},
  journal={The Lancet},
  volume={390},
  pages={S97},
  year={2017},
  publisher={Elsevier}
}

@article{jackson2018cogstack,
  title={CogStack-experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital},
  author={Jackson, Richard and Kartoglu, Ismail and Stringer, Clive and Gorrell, Genevieve and Roberts, Angus and Song, Xingyi and Wu, Honghan and Agrawal, Asha and Lui, Kenneth and Groza, Tudor and others},
  journal={BMC medical informatics and decision making},
  volume={18},
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  pages={1--13},
  year={2018},
  publisher={BioMed Central}
}

@misc{datatables,
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}

@misc{embase,
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	note="[Online; accessed 03-Mar-2021]"
}

@misc{healthmatch,
	author={HealthMatch},
	title={HealthMatch},
	howpublished = {\url{https://healthmatch.io/}},
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}

@article{miotto2013etacts,
  title={eTACTS: a method for dynamically filtering clinical trial search results},
  author={Miotto, Riccardo and Jiang, Silis and Weng, Chunhua},
  journal={Journal of biomedical informatics},
  volume={46},
  number={6},
  pages={1060--1067},
  year={2013},
  publisher={Elsevier}
}

@inproceedings{Rybinski:Karimi:Khoo:2021,
    author = {Rybinski, Maciej and Karimi, Sarvnaz and Khoo, Aleney},
    title = {Science2Cure: A Clinical Trial Search Prototype},
    year = {2021},
    booktitle = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
    pages = {2620--2624},
    keywords = {clinical trials search, learning-to-rank, evidence-based medicine, medical information retrieval, precision medicine},
}



@article{Rybinski:Dai:Singh:2021,
    author  = {Maciej Rybinski and Xiang Dai and Sonit Singh and Sarvnaz Karimi and Anthony  Nguyen},
    title   = {Extracting Family History Information From Electronic Health Records: Natural Language Processing Analysis},
    journal = {JMIR Medical Informatics},
    year    = {2021},
    volume  = {9},
    number  = {5},
    pages   = {e30153}
}

@inproceedings{Li:Zafeiriadis:2020,
author = {Li, Dan and Zafeiriadis, Panagiotis and Kanoulas, Evangelos},
title = "{APS: An Active PubMed Search System for Technology Assisted Reviews}",
year = {2020},
pages = {2137-–2140},
keywords = {TAR, active search, systematic reviews, PubMed},
}

@article{Panje:Glatzer:2018,
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numpages = {4},
keywords = {search, reproducibility, experimentation, clinical decision support},
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@ARTICLE{vax_hes2022,
  
AUTHOR={Raballo, Andrea and Poletti, Michele and Preti, Antonio},   
	 
TITLE={Vaccine Hesitancy, Anti-Vax, COVID-Conspirationism: From Subcultural Convergence to Public Health and Bioethical Problems},      
	
JOURNAL={Frontiers in Public Health},      
	
VOLUME={10},           
	
YEAR={2022},      
	  
URL={https://www.frontiersin.org/articles/10.3389/fpubh.2022.877490},       
	
DOI={10.3389/fpubh.2022.877490},      
	
ISSN={2296-2565}   
   
}

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  author    = {Seung{-}Hyeon Jo and
               Kyung{-}Soon Lee},
  editor    = {Ellen M. Voorhees and
               Angela Ellis},
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  year      = {2019},
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@inproceedings{julie-mug-19,
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  editor    = {Ellen M. Voorhees and
               Angela Ellis},
  title     = {{JULIE} {Lab} {\&} {Med} {Uni} {Graz} @ {TREC} 2019 {P}recision {M}edicine
               {T}rack},
  booktitle = {TREC},
  address = {Gaithersburg, MD},
  year      = {2019}
}

@inproceedings{ucas-trec18,
  author    = {Zhi Zheng and
               Canjia Li and
               Ben He and
               Jungang Xu},
  title     = {{UCAS} at {TREC-2018} {P}recision {M}edicine {T}rack},
  booktitle = {TREC},
  address = {Gaithersburg, MD},
  year      = {2018},
}

@inproceedings{brown-trec19,
  author    = {Abdullah Ahmed and
               Gil Alon and
               Bashar Zaidat and
               Isaac Nathoo and
               Hwai{-}Liang Tung and
               Charles Wang and
               Carsten Eickhoff},
  editor    = {Ellen M. Voorhees and
               Angela Ellis},
  title     = {Brown {U}niversity at {TREC} {P}recision {M}edicine 2019},
  booktitle	= {TREC}, 
  address	= {Gaithersburg, MD},
  year	= {2019},
  biburl    = {https://dblp.org/rec/conf/trec/AhmedAZNTWE19.bib},
}

@inproceedings{teckro-17,
  author    = {Johannes Leveling},
  title     = {Patient selection for clinical trials based on concept-based retrieval and result filtering and ranking},
  booktitle = {TREC},
  year      = {2017},
  address	= {Gaithersburg, MD}
}

@inproceedings{hpi-dhc-18,
  author    = {Michel Oleynik and
               Erik Faessler and
               Ariane Morassi Sasso and
               Arpita Kappattanavar and
               Benjamin Bergner and
               Harry Freitas Da Cruz and
               Jan{-}Philipp Sachs and
               Suparno Datta and
               Erwin P. B{\"{o}}ttinger},
  title     = {{HPI-DHC} at {TREC} 2018 {P}recision {M}edicine {T}rack},
  booktitle = {TREC},
  year      = {2018},
  address	= {Gaithersburg, MD}
}

@inproceedings{prna-mit-suny-2017,
  author    = {Yuan Ling and
               Sadid A. Hasan and
               Michele Filannino and
               Kevin P. Buchan and
               Kahyun Lee and
               Joey Liu and
               William Boag and
               Di Jin and
               {\"{O}}zlem Uzuner and
               Kathy Lee and
               Vivek V. Datla and
               Ashequl Qadir and
               Oladimeji Farri},
  title     = {A {H}ybrid {A}pproach to {P}recision {M}edicine-related {B}iomedical {A}rticle {R}etrieval and {C}linical {T}rial {M}atching},
  booktitle = {TREC},
  year  = {2017},
  address={Gaithersburg, MD}
}

@inproceedings{ims-unipd-18,
  author    = {Maristella Agosti and
               Giorgio Maria Di Nunzio and
               Stefano Marchesin},
  title     = {The {U}niversity of {P}adua {IMS} {R}esearch {G}roup at {TREC} 2018 {P}recision {M}edicine {T}rack},
  booktitle = {TREC},
  year      = {2018},
  address={Gaithersburg, MD}
}

@inproceedings{ims-unipd-19,
  author    = {Giorgio Maria Di Nunzio and
               Stefano Marchesin and
               Maristella Agosti},
  title     = {Exploring how to Combine Query Reformulations for Precision Medicine},
  booktitle = {TREC},
  year      = {2019},
  address={Gaithersburg, MD}
}

@inproceedings{garfield18,
  author    = {Xuesi Zhou and
               Xin Chen and
               Jian Song and
               Gang Zhao and
               Ji Wu},
  editor    = {Ellen M. Voorhees and
               Angela Ellis},
  title     = {Team {C}at-{G}arfield at {TREC} 2018 {P}recision {M}edicine {T}rack},
  booktitle = {TREC},
  year      = {2018},
  address={Gaithersburg, MD},
}

@inproceedings{delaware17,
  author    = {A. S. M. Ashique Mahmood and
               Gang Li and
               Shruti Rao and
               Peter B. McGarvey and
               Cathy H. Wu and
               Subha Madhavan and
               Vijay K. Shanker},
  title     = {{UD}{\_}{GU}{\_}{BioTM} at {TREC} 2017: {P}recision {M}edicine {T}rack},
  booktitle ={TREC},
  year      = {2017},
  address={Gaithersburg, MD}
}

@article{lee2019biobert,
    author = {Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
    title = {{BioBERT}: A pre-trained biomedical language representation model for biomedical text mining},
    journal = {Bioinformatics},
    year   = {2019},
    month  = {09},
    volume = {36},
    number = {4},
    pages  = {1234--1240},
}

@article{biobert,
    author = {Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
    title = {{BioBERT}: A pre-trained biomedical language representation model for biomedical text mining},
    journal = {Bioinformatics},
    year   = {2019},
    month  = {09},
    volume = {36},
    number = {4},
    pages  = {1234--1240},
}

@inproceedings{peng2019transfer,
  title={Transfer learning in biomedical natural language processing: {A}n evaluation of {BERT} and {ELMo} on ten benchmarking datasets},
  booktitle = {BioNLP 2019},
  address   = {Florence, Italy},
  author={Peng, Yifan and Yan, Shankai and Lu, Zhiyong},
  pages = {58--65},
  year={2019}
}

@inproceedings{liu19scut,
  author    = {Xiaofeng Liu and
               Lu Li and
               Zuoxi Yang and
               Shoubin Dong},
  title     = {{SCUT-CCNL} at {TREC} 2019 Precision Medicine Track},
  booktitle = {TREC},
  year      = {2019},
  address = {Gaithersburg, MD}
 
}

@article{qiao2019understanding,
  title={Understanding the {B}ehaviors of {BERT} in {R}anking},
  author={Qiao, Yifan and Xiong, Chenyan and Liu, Zhenghao and Liu, Zhiyuan},
  journal={arXiv preprint arXiv:1904.07531},
  year={2019}
}

@inproceedings{Song:He:Hu:2015,
	author	= {Y. Song and Y. He and Q. Hu and L. He},
	title	= {{ECNU} at 2015 {CDS} Track:Two Re-ranking Methods in Medical Information Retrieval},
	booktitle = {TREC},
	year	= {2015}
}

@inbook{Hersh:Detmer:2001,
	author	= {W. Hersh and W.M. Detmer and M.E. Frisse},
	title	= {Information-Retrieval Systems},
	booktitle = {Medical Informatics: Computer Applications in Health Care and Biomedicine},
	chapter	= {15},
	pages	= {539--572}
}

@inproceedings{Burges:2006,
	author = {Burges, C. and Ragno, R. and Le, Q. V.},
	title = {Learning to Rank with Nonsmooth Cost Functions},
	booktitle = {NIPS},
	year = {2006},
	address = {Canada},
	pages = {193--200}
} 

@inproceedings{Yilmaz:2006,
 author = {Yilmaz, E. and Aslam, J.A.},
 title = {Estimating Average Precision with Incomplete and Imperfect Judgments},
 booktitle = {CIKM},
 year = {2006},
 location = {Arlington, VA},
 pages = {102--111},
 keywords = {Bpref, average precision, evaluation, incomplete judgments, sampling}
} 

@inproceedings{Yilmaz:2008,
 author = {Yilmaz, Emine and Kanoulas, Evangelos and Aslam, Javed A},
 title = {A {S}imple and {E}fficient {S}ampling {M}ethod for {E}stimating {AP} and {NDCG}},
 booktitle = {SIGIR},
 year = {2008},
 address = {Singapore},
 pages = {603--610},
 keywords = {average precision, evaluation, incomplete judgments, infAP, nDCG, sampling}
} 

@inproceedings{cbnu:trec:2016,
	title = {{CBNU} at {TREC} 2016 Clinical Decision Support Track},
	author = {Jo, S. and Lee, K.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{CCNU:trec:2016,
	title = {{CCNU} at {TREC} 2016 Clinical Decision Support Track},
	author = {Yue Zhang, Y. and Hu, P. and Jian, F. },
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{DA_IICT:trec:2016,
	title = {Team {DA IICT} at Clinical Decision Support Track in {TREC} 2016: Topic Modeling for Query Expansion},
	author = {Sankhavara, J. and Majumder, P.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}


@inproceedings{MERCKKGAA:trec:2016,
	title = {Semi-Supervised Information Retrieval System for Clinical Decision Support},
	author = {Gurulingappa, H. and Toldo, L. and Schepers, C. and Bauer, A. and Megaro, G},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}


@inproceedings{nch_risi:trec:2016,
	title = {Evaluation of a Machine Learning Method to Rank PubMed Central Articles For Clinical Relevancy: {NCH} at {TREC} 2016 Clinical Decision Support Track},
	author = {Chen, W. and Moosavinasab, S. and Rust, S. and Huang, Y. and Lin, S. and Zemke, A. and Prinzbach, A.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}


@inproceedings{ECNU:tre:2016,
	title = {{ECNU} at TREC 2016: Web-based query expansion and experts diagnosis in Medical Information Retrieval},
	author = {Harsha Gurulingappa, Luca Toldo and Claudia Schepers},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{UDEL_fang:trec:2016,
	title = {Extracting Useful Information from Clinical Notes},
	author = {Wang, Y. and Fang, H.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{agosti2019analysis,
  title={An Analysis of Query Reformulation Techniques for Precision Medicine},
  author={Agosti, Maristella and Di Nunzio, Giorgio Maria and Marchesin, Stefano},
  booktitle={SIGIR},
  pages={973--976},
  address = {Paris, France},
  year={2019}
}

@inproceedings{Julie:trec:2019,
	title = {{JULIE} Lab at the 2019 {TREC} Precision Medicine Track},
	author = {Erik Faessler and Michel Oleynik},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2019}
}


@inproceedings{ETH:trec:2016,
	title = {{ETH} Zurich at {TREC} Clinical Decision Support 2016 },
	author = {Greuter, S. and Junker, P. and Kuhn, L. and Mance, F. and Mermet, V. and Rellstab, A. and Eickhoff, C.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}


@Article{Scholer:Kelly:Carterette:2016,
	author="Scholer, F. and Kelly, D. and Carterette, B.",
	title="Information retrieval evaluation using test collections",
	journal="Information Retrieval Journal",
	year="2016",
	volume="19",
	number="3",
	pages="225--229",
}

@inproceedings{EvALL:2017,
 author = {Amig\'{o}, Enrique and Carrillo-de-Albornoz, Jorge and Almagro-C\'{a}diz, Mario and Gonzalo, Julio and Rodr\'{\i}guez-Vidal, Javier and Verdejo, Felisa},
 title = {Ev{ALL}: Open Access Evaluation for Information Access Systems},
 booktitle = {SIGIR},
 series = {SIGIR '17},
 year = {2017},
 location = {Shinjuku, Tokyo, Japan},
 pages = {1301--1304},
 keywords = {evaluation, evaluation infrastructure, evaluation metrics, information access, information retrieval},
}

@inproceedings{Koopman:Cripwell:2017,
 author = {Koopman, Bevan and Cripwell, Liam and Zuccon, Guido},
 title = {Generating Clinical Queries from Patient Narratives: A Comparison Between Machines and Humans},
 booktitle = {SIGIR},
 year = {2017},
 address = {Shinjuku, Tokyo, Japan},
 pages = {853--856},
 keywords = {clinical search, information retrieval, query generation},
} 

@inproceedings{MayoNLPTeam:trec:2016,
	title =  {An Ensemble Model of Clinical Information Extraction and Information Retrieval for Clinical Decision Support},
	author = {Wang, Y. and Rastegar-Mojarad, M. and Komandur-Elayavilli, R. and Liu, S. and Liu, H.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016},
}

@inproceedings{DUTH:trec:2016,
	title =  {Augmenting Medical Queries with {UMLS} Concepts via MetaMap },
	author = {Agrafiotes, C. and Arampatzis, A. },
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{HAUT:trec:2016,
	title =  {Literature based Clinical Decision Support: Searching Articles According to Medical Needs},
	author = {Li, B. and Xiao, J. and Jiang, S. and Wang, Z. and Ding, H. and Niu, Y. },
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{IAII_PUT:trec:2016,
	title =  {{PUT} Contribution to {TREC CDS} 2016},
	author = {Dutkiewicz, J. and Jędrzejek, C. and Frąckowiak, M.  and Werda, P.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{iris:trec:2016,
	title =  {Query Expansion with Automatically Predicted Diagnosis: {iRiS} at {TREC CDS} track 2016},
	author = {Zhang, D. and He, D. and Zhao, S. and Li, L.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{IRIT:trec:2016,
	title =  {{IRIT @ TREC} 2016 Clinical Decision Support Track },
	author = {Nguyen, G. and Tamine, L. and Bricon-Souf, N.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{NKU:trec:2016,
	title =  {{NKU} at {TREC} 2016: Clinical Decision Support Track},
	author = {Zhang, H. and Liu, L.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{NLM_NIH:trec:2016,
	title =  {{NLM NIH} at {TREC} 2016 Clinical Decision Support Track},
	author = {Abacha, A.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{prna:trec:2016,
	title =  {Clinical Question Answering using Key-Value Memory Networks and Knowledge Graph },
	author = {Hasan, S. and Zhao, S. and Datla, V. and Liu, J. and Lee, K. and Qadir, A. and Prakash, A. and Farri, O.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{SCIAICLTeam:trec:2016,
	title =  {Siena's Clinical Decision Assistant with Machine Learning},
	author = {B. Kish and T. Walsh and K. Small and S. Gassert and K. Small and S. Gower Small},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{UNTIIA:trec:2016,
	title =  {{UNT} MEDICAL INFORMATION RETRIEVAL AT {TREC} 2016},
	author = {Viswavarapu, L. and Chen, J. and Cleveland, A. and Philbrick, J.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{WHUIRGroup:trec:2016,
	title =  {{WHUIRGroup} at {TREC} 2016 Clinical Decision Support Task},
	author = {Wang, R. and Ding, H. and Lu, W.},
	booktitle = {TREC},
	address = {Gaithersburg, MD},
	year = {2016}
}

@inproceedings{Balaneshin:Kordan:2016,
	author 	= {Balaneshin-kordan, Saeid and Kotov, Alexander},
	title 	= {Optimization Method for Weighting Explicit and Latent Concepts in Clinical Decision Support Queries},
	booktitle = {ICTIR},
	year = {2016},
	address	= {Newark, DE},
	pages = {241--250},
	keywords = {clinical decision support, feature-based retrieval models, global optimization, medical literature retrieval},
} 


% FIX THIS!
@inproceedings{Raja:Dasot:Goyal:2016,
	author	= {Kalpana Raja and Naman Dasot and Pawan Goyal and Siddhartha Jonnalagadda}, 
	title	= {Towards {E}vidence-based {P}recision {M}edicine: {E}xtracting {P}opulation {I}nformation from {B}iomedical {T}ext using {B}inary {C}lassifiers and {S}yntactic {P}atterns},
	booktitle= {AMIA}, 
	pages	= {203--212},
	year	= {2016},
}

@inproceedings{dai2019deeper,
  title={Deeper {T}ext {U}nderstanding for {IR} with {C}ontextual {N}eural {L}anguage {M}odeling},
  author={Dai, Zhuyun and Callan, Jamie},
  booktitle={SIGIR},
  pages={985--988},
  year={2019},
  address={Paris, France},
  url={https://dl.acm.org/doi/10.1145/3331184.3331303},
}

@inproceedings{yilmaz2019cross,
  title={Cross-domain modeling of sentence-level evidence for document retrieval},
  author={Yilmaz, Zeynep Akkalyoncu and Yang, Wei and Zhang, Haotian and Lin, Jimmy},
  booktitle=EMNLP-IJCNLP},
  pages={3481--3487},
  address={Hong Kong, China},
  url={https://www.aclweb.org/anthology/D19-1352/},
  year={2019}
}



@article{Lewis:Elmer:2016,
	author	= {Frey, Lewis and Bernstam, Elmer and Denny, Joshua},
	title 	= {Precision Medicine Informatics},
	journal = {Journal of the American Medical Informatics Association},
	volume 	= {23},
	number 	= {4},
	pages 	= {668--670},
	year 	= {2016},
}

@inproceedings{Guo:Fan:2016,
  author    = {Jiafeng Guo and
               Yixing Fan and
               Qingyao Ai and
               W. Bruce Croft},
  title     = {A Deep Relevance Matching Model for Ad-hoc Retrieval},
  booktitle = {CIKM}, 
  address   = {Indianapolis, IN},
  pages     = {55--64},
  year      = {2016},
  url = {https://www.aclweb.org/anthology/D18-1211.pdf}
}

@article{Pressler:Yen:2012,
author  = {Pressler, Taylor and Yen, Po-Yin and Ding, Jing and Liu, Jianhua and Embi, Peter and Payne, Philip},
title   = {Computational challenges and human factors influencing the design and use of clinical research participant eligibility pre-screening tools},
journal = {BMC Medical Informatics and Decision Making},
year    = {2012},
pages   = {47},
volume  = {12},
}

@article{Hassanzadeh:Karimi:Nguyen:2020,
    author  = {Hamed Hassanzadeh and Sarvnaz Karimi and Anthony Nguyen},
    title   = {Matching patients to clinical trials using semantically enriched document representation},
    journal = {Journal of Biomedical Informatics},
    volume  = {105},
    year    = {2020},
    pages  = {103406},
}

@article{Penberthy:2012,
    author  = {Penberthy, L. T., Dahman, B. A., Petkov, V. I., & DeShazo, J. P.},
    title   = {Effort required in eligibility screening for clinical trials},
    year    = {2012},
    journal = {Journal of oncology practice}, 
    volume  = {8},
    number  = {6},
    pages   = {365--370}
}

@inproceedings{Anserini:2017,
    author = {Yang, Peilin and Fang, Hui and Lin, Jimmy},
    title  = {Anserini: Enabling the Use of {L}ucene for Information Retrieval Research},
    year   = {2017},
    booktitle = {SIGIR},
    pages = {1253--1256},
    keywords = {reproducibility, open-source toolkits, multi-threaded inverted indexing, trec test collections},
    Address = {Tokyo, Japan},
}

@inproceedings{Galago:2012,
    title   = {Galago: A Modular Distributed Processing and Retrieval System},
    author  = {Marc-Allen Cartright and Samuel Huston and Henry Feild},
    booktitle = {OSIR@SIGIR},
    pages   = {25--31},
    year    = {2012}
}

@article{Karimi:Zobel:Scholer:2012,
    title   = {Quantifying the impact of concept recognition on biomedical information retrieval},
    journal = {Information Processing \& Management},
    author  = {Sarvnaz Karimi and Justin Zobel and Falk Scholer},
    volume  = {48},
    number  = {1},
    year    = {2012},
    pages   = {94--106},
}

@inproceedings{Terrier,
  author = {Iadh Ounis and Gianni Amati and Vassilis Plachouras and Ben He and  Craig Macdonald and Douglas Johnson},
  title = {Terrier Information Retrieval Platform},
  booktitle = {ECIR},
  volume = {3408},
  year = {2005},
  pages = {517--519},
}

@article{lin2020pretrained,
  title={Pretrained transformers for text ranking: {BERT} and beyond},
  author={Lin, Jimmy and Nogueira, Rodrigo and Yates, Andrew},
  journal={Computing Research Repository},
    eprint = {arXiv:2010.06467},
  year={2020}
}


@inproceedings{conneau2017,
    title = "Supervised Learning of Universal Sentence Representations from Natural Language Inference Data",
    author = {Conneau, Alexis  and
      Kiela, Douwe  and
      Schwenk, Holger  and
      Barrault, Lo{\"\i}c  and
      Bordes, Antoine},
    booktitle = "EMNLP",
    year = "2017",
    address = "Copenhagen, Denmark",
    pages = "670--680",
}


@inproceedings{ribeiro2016should,
  title={"Why Should I Trust You?" Explaining the predictions of any classifier},
  author={Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
  booktitle={Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining},
  pages={1135--1144},
  year={2016}
}

@misc{tformers,
  title = {Transformers},
  howpublished = {\url{https://huggingface.co/transformers/}},
  year  = {2021},
  note = {Accessed: 2021-02-22}
}

@misc{monologg,
  title = {BioBERT @ Huggingface model repository},
  howpublished = {\url{https://huggingface.co/monologg/biobert_v1.0_pubmed_pmc}},
  year = {2021},
  note = {Accessed: 2021-02-22}
}

@misc{drugbank,
  title = {DrugBank},
  howpublished = {\url{https://go.drugbank.com/}},
  year  = {2021},
  note = {Accessed: 2021-02-22}
}

@inproceedings{neumann2019,
    title = "{S}cispa{C}y: {F}ast and {R}obust {M}odels for {B}iomedical {N}atural {L}anguage {P}rocessing",
    author = "Neumann, Mark  and
      King, Daniel  and
      Beltagy, Iz  and
      Ammar, Waleed",
    booktitle = "BioNLP",
    year = "2019",
    address = "Florence, Italy",
    doi = "10.18653/v1/W19-5034",
    pages = "319--327",
    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{cross-domain-retrieval,
    title = "Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval",
    author = "Akkalyoncu Yilmaz, Zeynep  and
      Yang, Wei  and
      Zhang, Haotian  and
      Lin, Jimmy",
    booktitle = "EMNLP",
    year = "2019",
    address = "Hong Kong, China",
    pages = "3490--3496",
  url={https://www.aclweb.org/anthology/D19-1352/},
}

@article{hiles2008role,
  title={Role of sunitinib and sorafenib in the treatment of metastatic renal cell carcinoma},
  author={Hiles, Jon J and Kolesar, Jill M},
  journal={American Journal of Health-System Pharmacy},
  volume={65},
  number={2},
  pages={123--131},
  year={2008},
  publisher={Oxford University Press}
}

@article{milliken2019artpm,
  title={ARtPM: article retrieval for precision medicine},
  author={Milliken, Lowell K and Motomarry, Sirisha K and Kulkarni, Anagha},
  journal={Journal of biomedical informatics},
  volume={95},
  pages={103224},
  year={2019},
  publisher={Elsevier}
}

@article{hofstatter-bert-runtime,
    title = {Let's measure run time! {E}xtending the {IR} replicability infrastructure to include performance aspects},
    author = {Sebastian Hofstatter, Allan Hanbury},
    year = {2019},
    journal = {SIGIR 2019 Open-Source IR Replicability Challenge},

}

@inproceedings{lin-neural-hype,
author = {Yang, Wei and Lu, Kuang and Yang, Peilin and Lin, Jimmy},
title = {Critically Examining the “Neural Hype”: Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models},
year = {2019},
booktitle = {Proceedings of the 42nd international ACM SIGIR conference on Research and Development in Information Retrieval},
pages = {1129--1132},
numpages = {4},
keywords = {meta-analysis, document ranking, neural IR},
address = {Paris, France},
url={https://dl.acm.org/doi/10.1145/3331184.3331340}
}

@inproceedings{Gutierrez:covid:2020,
author = {Bernal Jimenez Gutierrez, Juncheng Zeng, Dongdong Zhang, Ping Zhang, Yu Su},
title = {Document Classification for COVID-19 Literature},
year = {2020},
address = {Online},
url = {https://openreview.net/pdf?id=SPxaJuM4Hbz},
series = {Proceedings of the NLP COVID-19 Workshop at ACL 2020}
}

@inproceedings{spangher:policy-twitter:2020,
author = {Alexander Spangher, Nanyun Peng, Jonathan May, Emilio Ferrara},
title = {Enabling Low-Resource Transfer Learning across COVID-19 Corpora by
Combining Event-Extraction and Co-Training},
year = {2020},
address = {Online},
url = {https://openreview.net/pdf?id=v8ioFR4fqpr},
series = {Proceedings of the NLP COVID-19 Workshop at ACL 2020}
}

@inproceedings{serrano:youtube-covid:2020,
author = {Juan Carlos Medina Serrano, Orestis Papakyriakopoulos, Simon Hegelich},
title = {NLP-based Feature Extraction for the Detection of COVID-19 Misinformation Videos on YouTube},
year = {2020},
address = {Online},
url = {https://openreview.net/forum?id=M4wgkxaPcyj},
series = {Proceedings of the NLP COVID-19 Workshop at ACL 2020}
}



@inproceedings{levy:outbreak-allignment-covid:2020,
author = {Sharon Levy, William Yang Wang},
title = {Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment},
year = {2020},
address = {Online},
url = {https://openreview.net/pdf?id=ub9_2iAo3D},
series = {Proceedings of the NLP COVID-19 Workshop at ACL 2020}
}

@inproceedings{alsudias:arabic-twitter:2020,
author = {Lama Alsudias, Paul Rayson},
title = {COVID-19 and Arabic Twitter: How can Arab World Governments and Public Health Organizations Learn from Social Media?},
year = {2020},
address = {Online},
url = {https://openreview.net/pdf?id=yx-k0ukHzDR},
series = {Proceedings of the NLP COVID-19 Workshop at ACL 2020}
}

@inproceedings{kruspe:sentiment-analysis-european:2020,
author = {Anna Kruspe, Matthias Haberle, Iona Kuhn, Xiao Xiang Zhu},
title = {Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic},
year = {2020},
address = {Online},
url = {https://openreview.net/pdf?id=VvRbhkiAwR},
series = {Proceedings of the NLP COVID-19 Workshop at ACL 2020}
}

@article{li2020parade,
    title={{PARADE:} Passage Representation Aggregation for Document Reranking},
    author={Canjia Li and Andrew Yates and Sean MacAvaney and Ben He and Yingfei Sun},
    year={2020},
    eprint={2008.09093},
    archivePrefix={arXiv},
    primaryClass={cs.IR},
    url = {https://arxiv.org/abs/2008.09093},
    journal={arXiv:2008.09093}
}

@inproceedings{Pradeep2022,
author = {Pradeep, Ronak and Li, Yilin and Wang, Yuetong and Lin, Jimmy},
title = {Neural Query Synthesis and Domain-Specific Ranking Templates for Multi-Stage Clinical Trial Matching},
year = {2022},
doi = {10.1145/3477495.3531853},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2325–2330},
keywords = {trec, sequence-to-sequence models},
address = {Madrid, Spain},
series = {SIGIR '22}
}



@article{edwin2020evaluation,
    title={Covidex: Neural Ranking Models and Keyword Search Infrastructure for the {COVID-19} Open Research Dataset},
    author={Edwin Zhang and Nikhil Gupta and Raphael Tang and Xiao Han and Ronak Pradeep and Kuang Lu and Yue Zhang and Rodrigo Nogueira and Kyunghyun Cho and Hui Fang and Jimmy Lin},
    year={2020},
    eprint={2007.0784},
    archivePrefix={arXiv},
    primaryClass={cs.IR},
    journal={arXiv:2007.0784},
    url = {https://arxiv.org/abs/2007.07846}
}

@inproceedings{bingsearch,
author = {Wang, Lidan and Lin, Jimmy and Metzler, Donald},
title = {A Cascade Ranking Model for Efficient Ranked Retrieval},
year = {2011},
doi = {10.1145/2009916.2009934},
booktitle = {SIGIR},
pages = {105–114},
keywords = {effectiveness, learning to rank, efficiency},
address = {Beijing, China},
}

@inproceedings{Liu2017CascadeRF,
    author = {Liu, Shichen and Xiao, Fei and Ou, Wenwu and Si, Luo},
    title = {Cascade Ranking for Operational E-Commerce Search},
    year = {2017},
    address = {New York, NY, USA},
    doi = {10.1145/3097983.3098011},
    booktitle = {SIGKDD},
    pages = {1557–1565},
    numpages = {9},
    keywords = {user experience, operational e-commerce search system, cascade ranking, effectiveness and efficiency},
    location = {Halifax, Canada},
    publisher = {Association for Computing Machinery}
}

@inproceedings{queries_vs_questions,
author = {White, Ryen W. and Richardson, Matthew and Yih, Wen-tau},
title = {Questions vs. Queries in Informational Search Tasks},
year = {2015},
isbn = {9781450334730},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2740908.2742769},
doi = {10.1145/2740908.2742769},
booktitle = {Proceedings of the 24th International Conference on World Wide Web},
pages = {135–136},
numpages = {2},
keywords = {query formulation, informational search, natural language queries},
location = {Florence, Italy},
series = {WWW '15 Companion}
}


@article{Fogel:Clinical:2018,    author = {David B Fogel},    title = {Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review},    journal = {Contemporary clinical trials communications},    pages = {156-164},    year = {2018}}

@article{Sackett:Rosenberg:BMJ:1996,    author = {David L Sackett and William MC Rosenberg and JA Muir Gray and R Brian Haynes and W Scott Richardson},    title = {Evidence based medicine: what it is and what it isn't},    journal = {The BMJ},       year = {1996}}

@article{Wang:Zhang:Lancet:2020,    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},    title = {Remdesivir in adults with severe {COVID-19}: a randomised, double-blind, placebo-controlled, multicentre trial},    journal = {The Lancet},    pages = {1569-1578},    year = {2020}}

@article{Shen:Liu:JMIR:2021,    author = {Feichen Shen and Sijia Liu and Sunyang Fu and Yanshan Wang and Sam Henry and Ozlem Uzuner and Hongfang Liu},    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},    journal = {JMIR Medical Informatics},    pages = {e24008},    year = {2021}}

@book{Sutton:McCallum:2007,    author = {Charles Sutton and Andrew McCallum},    title = {An introduction to conditional random fields for relational learning},          year = {2007},   booktitle = {Introduction to statistical relational learning},   publisher = {The MIT Press}}

@inproceedings{Gururangan:Marasovic:ACL:2020,    author = {Suchin Gururangan and Ana Marasović and Swabha Swayamdipta and Kyle Lo and Iz Beltagy and Doug Downey and Noah A Smith},    title = {Don't Stop Pretraining: Adapt Language Models to Domains and Tasks},    booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},    address = {Online},    pages = {8342--8360},    year = {2020}}

@inproceedings{Alsentzer:Murphy:ClinicalNLP:2019,    author = {Emily Alsentzer and John R Murphy and Willie Boag and Wei-Hung Weng and Di Jin and Tristan Naumann and Matthew McDermott},    title = {Publicly available clinical {BERT} embeddings},    booktitle = {Proceedings of the 2nd Clinical Natural Language Processing Workshop},    address = {Minneapolis, Minnesota},    pages = {72--78},    year = {2019}}

@inproceedings{Pruksachatkun:Phang:ACL:2020,    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},    title = {Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?},    booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},    address = {Online},    pages = {5231--5247},    year = {2020}}

@article{Dogan:Leaman:JBI:2014,    author = {Rezarta Islamaj Do{\u{g}}an and Robert Leaman and Zhiyong Lu},    title = {{NCBI} disease corpus: a resource for disease name recognition and concept normalization},    journal = {Journal of biomedical informatics},    pages = {1-10},    year = {2014}}

@article{Cer:Yang:arXiv:2018,    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},    title = {Universal sentence encoder},    journal = {arXiv preprint arXiv:1803.11175},       year = {2018}}

@article{Henry:Wang:JMIA:2019,
    author = {Sam Henry and Yanshan Wang and Feichen Shen and Ozlem Uzuner},
    title = {The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task on clinical concept normalization for clinical records},    
    journal = {Journal of the American Medical Informatics Association},
    year = {2020}
}

@article{Loos:Bouchard:InternMed:2003,    author = {Ruth JF Loos and Claude Bouchard},    title = {Obesity–is it a genetic disorder?},    journal = {Journal of internal medicine},    pages = {401-425},    year = {2003}}

@article{Bullo:Garcia-Aloy:PrevMed:2011,    author = {Mònica Bulló and Mar Garcia-Aloy and Miguel A Martínez-González and Dolores Corella and Joan D Fernández-Ballart and Miquel Fiol and Enrique Gómez-Gracia and Ramón Estruch and Manuel Ortega-Calvo and Silvia Francisco},    title = {Association between a healthy lifestyle and general obesity and abdominal obesity in an elderly population at high cardiovascular risk},    journal = {Preventive medicine},    pages = {155-161},    year = {2011}}

@article{Henry:Wang:JMIA:2020,    author = {Sam Henry and Yanshan Wang and Feichen Shen and Ozlem Uzuner},    title = {The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task on clinical concept normalization for clinical records},    journal = {Journal of the American Medical Informatics Association},    pages = {1529–1537},    year = {2020}}

@ARTICLE{Novitzke2008-ol,
  title    = "The significance of clinical trials",
  author   = "Novitzke, Jill M",
  abstract = "BACKGROUND: Clinical trials are essential for the development of
              new treatments. Whether a person should participate depends on
              their understanding of the risks and benefits for themselves and
              for society as a whole. DISCUSSION: There are rules in place to
              protect human research subjects and all studies involving humans
              are reviewed locally to ensure that subjects are treated safely,
              fairly, and confidentially. Nevertheless, each subject should
              consider for themselves whether participation is consistent with
              their values. CONCLUSION: Clinical trials, when well-designed,
              can benefit the participants as well as the investigators, the
              sponsors, and the medical community.",
  journal  = "J Vasc Interv Neurol",
  volume   =  1,
  number   =  1,
  pages    = "31",
  month    =  jan,
  year     =  2008,
  keywords = "clinical trial; research",
  language = "en"
}

@ARTICLE{Sacristan2016-cj,
  title    = "Patient involvement in clinical research: why, when, and how",
  author   = "Sacrist{\'a}n, Jos{\'e} A and Aguar{\'o}n, Alfonso and
              Avenda{\~n}o-Sol{\'a}, Cristina and Garrido, Pilar and
              Carri{\'o}n, Juan and Guti{\'e}rrez, Alipio and Kroes, Robert and
              Flores, Angeles",
  abstract = "The development of a patient-centered approach to medicine is
              gradually allowing more patients to be involved in their own
              medical decisions. However, this change is not happening at the
              same rate in clinical research, where research generally
              continues to be carried out on patients, but not with patients.
              This work describes the why, when, and how of more active patient
              participation in the research process. Specific measures are
              proposed to improve patient involvement in 1) setting priorities,
              2) study leadership and design, 3) improved access to clinical
              trials, 4) preparation and oversight of the information provided
              to participants, 5) post-study evaluation of the patient
              experience, and 6) the dissemination and application of results.
              In order to achieve these aims, the relative emphases on the
              ethical principles underlying research need to be changed. The
              current model based on the principle of beneficence must be left
              behind, and one that upholds the ethical principles of autonomy
              and non maleficence should be embraced. There is a need to
              improve the level of information that patients and society as a
              whole have on research objectives and processes; the goal is to
              promote the gradual emergence of the expert patient.",
  journal  = "Patient Prefer Adherence",
  volume   =  10,
  pages    = "631--640",
  month    =  apr,
  year     =  2016,
  keywords = "bioethics; clinical trials; engagement; patients; research",
  language = "en"
}


@article{clinical_trials_participation,
author = {Unger, Joseph M. and Hershman, Dawn L. and Albain, Kathy S. and Moinpour, Carol M. and Petersen, Judith A. and Burg, Kenda and Crowley, John J.},
title = {Patient Income Level and Cancer Clinical Trial Participation},
journal = {Journal of Clinical Oncology},
volume = {31},
number = {5},
pages = {536-542},
year = {2013},
doi = {10.1200/JCO.2012.45.4553},
    note ={PMID: 23295802},

URL = { 
    
        https://doi.org/10.1200/JCO.2012.45.4553
    
    

},
eprint = { 
    
        https://doi.org/10.1200/JCO.2012.45.4553
}
,
    abstract = { Purpose Studies have shown an association between socioeconomic status (SES) and quality of oncology care, but less is known about the impact of patient SES on clinical trial participation. Patients and Methods We assessed clinical trial participation patterns according to important SES (income, education) and demographic factors in a large sample of patients surveyed via an Internet-based treatment decision tool. Logistic regression, conditioning on type of cancer, was used. Attitudes toward clinical trials were assessed using prespecified items about treatment, treatment tolerability, convenience, and cost. Results From 2007 to 2011, 5,499 patients were successfully surveyed. Forty percent discussed clinical trials with their physician, 45\% of discussions led to physician offers of clinical trial participation, and 51\% of offers led to clinical trial participation. The overall clinical trial participation rate was 9\%. In univariate models, older patients (P = .002) and patients with lower income (P = .001) and education (P = .02) were less likely to participate in clinical trials. In a multivariable model, income remained a statistically significant predictor of clinical trial participation (odds ratio, 0.73; 95\% CI, 0.57 to 0.94; P = .01). Even in patients age ≥ 65 years, who have universal access to Medicare, lower income predicted lower trial participation. Cost concerns were much more evident among lower-income patients (P < .001). Conclusion Lower-income patients were less likely to participate in clinical trials, even when considering age group. A better understanding of why income is a barrier may help identify ways to make clinical trials better available to all patients and would increase the generalizability of clinical trial results across all income levels. }
}

@ARTICLE{Unger2021-mz,
  title    = "``When Offered to Participate'': A Systematic Review and
              {Meta-Analysis} of Patient Agreement to Participate in Cancer
              Clinical Trials",
  author   = "Unger, Joseph M and Hershman, Dawn L and Till, Cathee and
              Minasian, Lori M and Osarogiagbon, Raymond U and Fleury, Mark E
              and Vaidya, Riha",
  abstract = "BACKGROUND: Patient participation in clinical trials is vital for
              knowledge advancement and outcomes improvement. Few adult cancer
              patients participate in trials. Although patient. decision-making
              about trial participation has been frequently examined, the
              participation rate for patients actually offered a trial is
              unknown. METHODS: A systematic review and meta-analysis using 3
              major search engines was undertaken. We identified studies from
              January 1, 2000, to January 1, 2020, that examined clinical trial
              participation in the United States. Studies must have specified
              the numbers of patients offered a trial and the number enrolled.
              A random effects model of proportions was used. All statistical
              tests were 2-sided. RESULTS: We identified 35 studies (30 about
              treatment trials and 5 about cancer control trials) among which
              9759 patients were offered trial participation. Overall, 55.0\%
              (95\% confidence interval [CI] = 49.4\% to 60.5\%) of patients
              agreed to enroll. Participation rates did not differ between
              treatment (55.0\%, 95\% CI = 48.9\% to 60.9\%) and cancer control
              trials (55.3\%, 95\% CI = 38.9\% to 71.1\%; P = .98). Black
              patients participated at similar rates (58.4\%, 95\% CI = 46.8\%
              to 69.7\%) compared with White patients (55.1\%, 95\% CI = 44.3\%
              to 65.6\%; P = .88). The main reasons for nonparticipation were
              treatment choice or lack of interest. CONCLUSIONS: More than half
              of all cancer patients offered a clinical trial do participate.
              These findings upend several conventional beliefs about cancer
              clinical trial participation, including that Black patients are
              less likely to agree to participate and that patient
              decision-making is the primary barrier to participation. Policies
              and interventions to improve clinical trial participation should
              focus more on modifiable systemic structural and clinical
              barriers, such as improving access to available trials and
              broadening eligibility criteria.",
  journal  = "J Natl Cancer Inst",
  volume   =  113,
  number   =  3,
  pages    = "244--257",
  month    =  mar,
  year     =  2021,
  language = "en"
}

@inproceedings{adam_optimiser,
author = {Diederik P. Kingma and Jimmy Ba},
editor    = {Yoshua Bengio and
               Yann LeCun},
title = {Adam: {A} Method for Stochastic Optimization},
year = {2015},
address = {San Diego, CA, USA},
url = {http://arxiv.org/abs/1412.6980},
booktitle = {3rd International Conference on Learning Representations},
}

@inproceedings{yang-etal-2019-end-end,
    title = "End-to-End Open-Domain Question Answering with {BERT}serini",
    author = "Yang, Wei  and
      Xie, Yuqing  and
      Lin, Aileen  and
      Li, Xingyu  and
      Tan, Luchen  and
      Xiong, Kun  and
      Li, Ming  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    url = "https://aclanthology.org/N19-4013",
    doi = "10.18653/v1/N19-4013",
    pages = "72--77",
    abstract = "We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit. In contrast to most question answering and reading comprehension models today, which operate over small amounts of input text, our system integrates best practices from IR with a BERT-based reader to identify answers from a large corpus of Wikipedia articles in an end-to-end fashion. We report large improvements over previous results on a standard benchmark test collection, showing that fine-tuning pretrained BERT with SQuAD is sufficient to achieve high accuracy in identifying answer spans.",
}

@inproceedings{colbert,
author = {Khattab, Omar and Zaharia, Matei},
title = {ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT},
year = {2020},
isbn = {9781450380164},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3397271.3401075},
doi = {10.1145/3397271.3401075},
booktitle = {Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {39–48},
numpages = {10},
keywords = {efficiency, bert, neural ir, deep language models},
location = {Virtual Event, China},
series = {SIGIR '20}
}

@inproceedings{hui-etal-2017-pacrr,
    title = "{PACRR}: A Position-Aware Neural {IR} Model for Relevance Matching",
    author = "Hui, Kai  and
      Yates, Andrew  and
      Berberich, Klaus  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    url = "https://aclanthology.org/D17-1110",
    doi = "10.18653/v1/D17-1110",
    pages = "1049--1058",
    abstract = "In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR aiming at better modeling position-dependent interactions between a query and a document. Extensive experiments on six years{'} TREC Web Track data confirm that the proposed model yields better results under multiple benchmarks.",
}

@inproceedings{ma-etal-2021-contrastive,
    title = "Contrastive Fine-tuning Improves Robustness for Neural Rankers",
    author = "Ma, Xiaofei  and
      Nogueira dos Santos, Cicero  and
      Arnold, Andrew O.",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    
    url = "https://aclanthology.org/2021.findings-acl.51",
    doi = "10.18653/v1/2021.findings-acl.51",
    pages = "570--582",
}

@inproceedings{gao-callan-2022-unsupervised,
    title = "Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval",
    author = "Gao, Luyu  and
      Callan, Jamie",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    
    url = "https://aclanthology.org/2022.acl-long.203",
    doi = "10.18653/v1/2022.acl-long.203",
    pages = "2843--2853",
    abstract = "Recent research demonstrates the effectiveness of using fine-tuned language models (LM) for dense retrieval. However, dense retrievers are hard to train, typically requiring heavily engineered fine-tuning pipelines to realize their full potential. In this paper, we identify and address two underlying problems of dense retrievers: i) fragility to training data noise and ii) requiring large batches to robustly learn the embedding space. We use the recently proposed Condenser pre-training architecture, which learns to condense information into the dense vector through LM pre-training. On top of it, we propose coCondenser, which adds an unsupervised corpus-level contrastive loss to warm up the passage embedding space. Experiments on MS-MARCO, Natural Question, and Trivia QA datasets show that coCondenser removes the need for heavy data engineering such as augmentation, synthesis, or filtering, and the need for large batch training. It shows comparable performance to RocketQA, a state-of-the-art, heavily engineered system, using simple small batch fine-tuning.",
}

@inproceedings{Luo2022ImprovingBI,
  title={Improving Biomedical Information Retrieval with Neural Retrievers},
  author={Man Luo and Arindam Mitra and Tejas Gokhale and Chitta Baral},
  booktitle={AAAI Conference on Artificial Intelligence},
  year={2022}
}

@inproceedings{realm-cold-start,
    author = {Guu, Kelvin and Lee, Kenton and Tung, Zora and Pasupat, Panupong and Chang, Ming-Wei},
    title = {REALM: Retrieval-Augmented Language Model Pre-Training},
    year = {2020},
    booktitle = {Proceedings of the 37th International Conference on Machine Learning},
    articleno = {368},
    numpages = {10},
    series = {ICML'20},
    address = {Online},
}

@inproceedings{wang-etal-2018-glue,
    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",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    
    url = "https://aclanthology.org/W18-5446",
    doi = "10.18653/v1/W18-5446",
    pages = "353--355",
    abstract = "Human ability to understand language is \textit{general, flexible, and robust}. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions.",
}

@inproceedings{transformer_original,
 author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, \L ukasz and Polosukhin, Illia},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Attention is All you Need},
 url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf},
 volume = {30},
 year = {2017}
}


@misc{virus_vs_bacteria_csiro_outbreak,
 author = {Outbreak Project},
 url = {https://outbreakproject.com.au/2020/05/14/public-information-key-to-combatting-rising-superbug-threat/},
 year = {2020},
 urldate = {2023-04-19},
 publisher = {CSIRO}
}

@inbook{cranfield_experiments,
author = {Cleverdon, Cyril},
title = {The Cranfield Tests on Index Language Devices},
year = {1997},
isbn = {1558604545},
publisher = {Morgan Kaufmann Publishers Inc.},
address = {San Francisco, CA, USA},
booktitle = {Readings in Information Retrieval},
pages = {47–59},
numpages = {13}
}

@InProceedings{relevance_judgement_nist,
author="Voorhees, Ellen M.",
editor="Peters, Carol
and Braschler, Martin
and Gonzalo, Julio
and Kluck, Michael",
title="The Philosophy of Information Retrieval Evaluation",
booktitle="Evaluation of Cross-Language Information Retrieval Systems",
year="2002",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="355--370",
abstract="Evaluation conferences such as TREC, CLEF, and NTCIR are modern examples of the Cranfield evaluation paradigm. In Cranfield, researchers perform experiments on test collections to compare the relative effectiveness of different retrieval approaches. The test collections allow the researchers to control the effects of different system parameters, increasing the power and decreasing the cost of retrieval experiments as compared to user-based evaluations. This paper reviews the fundamental assumptions and appropriate uses of the Cranfield paradigm, especially as they apply in the context of the evaluation conferences.",
isbn="978-3-540-45691-9"
}

@article{qa_domain_definition,
author = {Allam, Ali and Haggag, Mohamed},
year = {2012},
month = {09},
pages = {211-221},
title = {The Question Answering Systems: A Survey},
volume = {2},
journal = {International Journal of Research and Reviews in Information Sciences}
}

@inproceedings{mooney_qa_WOLFIE,
  author       = {Cynthia A. Thompson and
                  Raymond J. Mooney},
  editor       = {Eugene Charniak},
  title        = {Semantic Lexicon Acquisition for Learning Natural Language Interfaces},
  booktitle    = {Sixth Workshop on Very Large Corpora, VLC@COLING/ACL 1998, Montreal,
                  Quebec, Canada, August 15-16, 1998},
  year         = {1998},
  url          = {https://aclanthology.org/W98-1107/},
  timestamp    = {Fri, 06 Aug 2021 00:41:36 +0200},
  biburl       = {https://dblp.org/rec/conf/acl-vlc/ThompsonM98.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{trec1999qa,
  editor       = {Ellen M. Voorhees},
  title        = {The TREC-8 Question Answering Track Report},
  series       = {{NIST} Special Publication},
  volume       = {500-246},
  address = {Gaithersburg, Maryland, USA},
  publisher    = {National Institute of Standards and Technology {(NIST)}},
  year         = {1999},
  url          = {https://trec.nist.gov/pubs/trec8/papers/qa_report.pdf},
}

@Book{wordnet,
  title = {WordNet: An Electronic Lexical Database},
  author = {Christiane Fellbaum},
  year = {1998},
  publisher = {Bradford Books},
  url = {https://mitpress.mit.edu/9780262561167/},
}

@inproceedings{trec12qa,
  author       = {Ellen M. Voorhees},
  editor       = {Ellen M. Voorhees and
                  Lori P. Buckland},
  title        = {Overview of the {TREC} 2003 Question Answering Track},
  series       = {{NIST} Special Publication},
  address = {Gaithersburg, Maryland, USA},
  publisher    = {National Institute of Standards and Technology {(NIST)}},
  booktitle = {TREC},
  year         = {2003},
  url          = {http://trec.nist.gov/pubs/trec12/papers/QA.OVERVIEW.pdf},
}

@inproceedings{trec13qa,
    author       = {Ellen M. Voorhees},
    editor       = {Ellen M. Voorhees and
                  Lori P. Buckland},
    title        = {Overview of the {TREC} 2004 Question Answering Track},
    address =     {Gaithersburg, Maryland, USA},
    series       = {{NIST} Special Publication},
    publisher    = {National Institute of Standards and Technology {(NIST)}},
  booktitle = {TREC},
    year         = {2004},
    url          = {http://trec.nist.gov/pubs/trec13/papers/QA.OVERVIEW.pdf},
}

@inproceedings{trec14qa,
    author       = {Ellen M. Voorhees},
    editor       = {Ellen M. Voorhees and
                  Lori P. Buckland},
    title        = {Overview of the {TREC} 2005 Question Answering Track},
    address =     {Gaithersburg, Maryland, USA},
    series       = {{NIST} Special Publication},
    publisher    = {National Institute of Standards and Technology {(NIST)}},
    year         = {2005},
  booktitle = {TREC},
    url          = {http://trec.nist.gov/pubs/trec14/papers/QA.OVERVIEW.pdf},
}

@InProceedings{CLEF2003,
author="Magnini, Bernardo
    and Romagnoli, Simone
    and Vallin, Alessandro
    and Herrera, Jes{\'u}s
    and Pe{\~{n}}as, Anselmo
    and Peinado, V{\'i}ctor
    and Verdejo, Felisa
    and de Rijke, Maarten",
    editor="Peters, Carol
    and Gonzalo, Julio
    and Braschler, Martin
    and Kluck, Michael",
title="The Multiple Language Question Answering Track at CLEF 2003",
booktitle="Comparative Evaluation of Multilingual Information Access Systems",
year="2004",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="471--486",
abstract="This paper reports on the pilot question answering track that was carried out within the CLEF initiative this year. The track was divided into monolingual and bilingual tasks: monolingual systems were evaluated within the frame of three non-English European languages, Dutch, Italian and Spanish, while in the cross-language tasks an English document collection constituted the target corpus for Italian, Spanish, Dutch, French and German queries. Participants were given 200 questions for each task, and were allowed to submit up to two runs per task with up to three responses (either exact answers or 50 byte long strings) per question.",
isbn="978-3-540-30222-3"
}

@inproceedings{trec10,
  author       = {Ellen M. Voorhees},
  editor       = {Ellen M. Voorhees and
                  Donna K. Harman},
  title        = {Overview of the {TREC} 2001 Question Answering Track},
  booktitle    = {Proceedings of The Tenth Text REtrieval Conference, {TREC} 2001, Gaithersburg,
                  Maryland, USA, November 13-16, 2001},
  series       = {{NIST} Special Publication},
  volume       = {500-250},
  publisher    = {National Institute of Standards and Technology {(NIST)}},
  year         = {2001},
  url          = {http://trec.nist.gov/pubs/trec10/papers/qa10.pdf},
  timestamp    = {Wed, 07 Jul 2021 16:44:22 +0200},
  biburl       = {https://dblp.org/rec/conf/trec/Voorhees01a.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{universal_approx_theorem,
title = {Multilayer feedforward networks are universal approximators},
journal = {Neural Networks},
volume = {2},
number = {5},
pages = {359-366},
year = {1989},
issn = {0893-6080},
doi = {https://doi.org/10.1016/0893-6080(89)90020-8},
url = {https://www.sciencedirect.com/science/article/pii/0893608089900208},
author = {Kurt Hornik and Maxwell Stinchcombe and Halbert White},
keywords = {Feedforward networks, Universal approximation, Mapping networks, Network representation capability, Stone-Weierstrass Theorem, Squashing functions, Sigma-Pi networks, Back-propagation networks},
abstract = {This paper rigorously establishes that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available. In this sense, multilayer feedforward networks are a class of universal approximators.}
}

@techreport{graceful_qa_cecile,
    author = {Cecile L. Paris},
    title = {Towards More Graceful Interaction: a Survey of Question-Answering Programs.},
    institution = {Department of Computer Science, Columbia University},
    year = {1985},
}

@incollection{grice_theory_logic,
  title={Logic and conversation},
  author={Grice, Herbert Paul},
  booktitle={Speech Acts},
  pages={41--58},
  year={1975},
  publisher={Brill}
}

@article{grice_theory_of_utterence,
    author = {Herbert Paul Grice},
    title = {Uterrer\'s Meaning and Intentions},
    journal = {Philosophical Review},
    volume = {68},
    year = {1969},
    pages = {147 - 177},
}

#15. Grice, H. P. "Utterer's Meaning and Intentions". PJrilosopJticlJl Review 68, 2 (1969), 147 - 177.
#16. Grice, H. P. Logic and Calveaation. In Synuu and SDnQIllics, P. Cole and I. L. Morgan, Ed., Academic Press, New York. 1975.

@inproceedings{bioasq-runnerup-2017,
    title = "A Biomedical Question Answering System in {B}io{ASQ} 2017",
    author = "Sarrouti, Mourad  and
      Ouatik El Alaoui, Said",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    
    url = "https://aclanthology.org/W17-2337",
    doi = "10.18653/v1/W17-2337",
    pages = "296--301",
    abstract = "Question answering, the identification of short accurate answers to users questions, is a longstanding challenge widely studied over the last decades in the open domain. However, it still requires further efforts in the biomedical domain. In this paper, we describe our participation in phase B of task 5b in the 2017 BioASQ challenge using our biomedical question answering system. Our system, dealing with four types of questions (i.e., yes/no, factoid, list, and summary), is based on (1) a dictionary-based approach for generating the exact answers of yes/no questions, (2) UMLS metathesaurus and term frequency metric for extracting the exact answers of factoid and list questions, and (3) the BM25 model and UMLS concepts for retrieving the ideal answers (i.e., paragraph-sized summaries). Preliminary results show that our system achieves good and competitive results in both exact and ideal answers extraction tasks as compared with the participating systems.",
}

@inproceedings{molla-2018bioasq,
    title = "{M}acquarie {U}niversity at {B}io{ASQ} 6b: Deep learning and deep reinforcement learning for query-based summarisation",
    author = "Moll{\'a}, Diego",
    booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    
    url = "https://aclanthology.org/W18-5303",
    doi = "10.18653/v1/W18-5303",
    pages = "22--29",
    abstract = "This paper describes Macquarie University{'}s contribution to the BioASQ Challenge (BioASQ 6b, Phase B). We focused on the extraction of the ideal answers, and the task was approached as an instance of query-based multi-document summarisation. In particular, this paper focuses on the experiments related to the deep learning and reinforcement learning approaches used in the submitted runs. The best run used a deep learning model under a regression-based framework. The deep learning architecture used features derived from the output of LSTM chains on word embeddings, plus features based on similarity with the query, and sentence position. The reinforcement learning approach was a proof-of-concept prototype that trained a global policy using REINFORCE. The global policy was implemented as a neural network that used tf.idf features encoding the candidate sentence, question, and context.",
}

@ARTICLE{snowmed-orig,
  title    = "{SNOMED} clinical terms: overview of the development process and
              project status",
  author   = "Stearns, M Q and Price, C and Spackman, K A and Wang, A Y",
  abstract = "Two large health care reference terminologies, SNOMED RT and
              Clinical Terms Version 3 , are in the process of being merged to
              form a comprehensive new work referred to as SNOMED Clinical
              Terms. The College of American Pathologists and the United
              Kingdom s National Health Service have entered into a
              collaborative agreement to develop this new work. Both
              organizations have extensive terminology development and
              maintenance experience. This paper discusses the process and
              status of SNOMED CT development and how the resources and
              expertise of both organizations are being used to develop this
              new terminological resource. The preliminary results of the
              merger process, including mapping, the merger of upper levels of
              each hierarchy, and attribute harmonization are also discussed.",
  journal  = "Proc AMIA Symp",
  pages    = "662--666",
  year     =  2001,
  address  = "United States",
  language = "en"
}

@ARTICLE{snowmed-lit-review,
  title    = "Forty years of {SNOMED}: a literature review",
  author   = "Cornet, Ronald and de Keizer, Nicolette",
  abstract = "BACKGROUND: Over a period of 40 years, SNOMED has developed from
              a pathology-specific nomenclature (SNOP) into a logic-based
              health care terminology. In spite of its long existence and
              continuous evolvement, it is yet unknown to what extent SNOMED is
              used in clinical practice, and what benefits were achieved. The
              aim of this paper is to investigate this by providing an overview
              of published studies in which a version of SNOMED was studied or
              applied. METHODS: This paper analyzes the use of SNOMED over
              time, as reflected in scientific publications, by means of
              Medline literature search in PubMed. The search included papers
              from 1966 until June 2006. For each selected paper the following
              characteristics were classified: version, medical domain, coding
              moment (during or after the care process), usage, and type of
              evaluation (e.g., does SNOMED work, does SNOMED help). RESULTS:
              250 papers were included in this research. The number of annual
              publications has increased, as has the number of domains in which
              SNOMED is being used. Theoretical studies mainly concern
              comparison of SNOMED to other terminological systems and SNOMED
              as an illustration of a terminological systems' theory. Few
              studies are available on the usage of SNOMED in clinical
              practice, largely involving coding information and
              retrieval/aggregation based on SNOMED codes. CONCLUSION: The
              clinical application of SNOMED is broadening beyond pathology.
              The majority of studies concern proving the value of SNOMED in
              theory. Fewer studies are available on the usage of SNOMED in
              clinical practice. Literature gives no indication of the use of
              SNOMED for direct care purposes such as decision support.",
  journal  = "BMC Med Inform Decis Mak",
  volume   = "8 Suppl 1",
  number   = "Suppl 1",
  pages    = "S2",
  month    =  oct,
  year     =  2008,
  address  = "England",
  language = "en"
}

@inproceedings{CoT_paper,
 author = {Wei, Jason and Wang, Xuezhi and Schuurmans, Dale and Bosma, Maarten and ichter, brian and Xia, Fei and Chi, Ed and Le, Quoc V and Zhou, Denny},
 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 = {24824--24837},
 publisher = {Curran Associates, Inc.},
 title = {Chain-of-Thought Prompting Elicits Reasoning in Large Language Models},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}

@inproceedings{self-consistency,
    author = {Xuezhi Wang and Jason Wei and Dale Schuurmans† and Quoc Le and Ed H. Chi and Sharan Narang and Aakanksha Chowdhery and Denny Zhou†},
    title = {SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS},
    booktitle = {Eleventh International Conference on Learning Representations},
    url = {https://arxiv.org/pdf/2203.11171.pdf},
    year = {2023}
}

@inproceedings{least-to-most,
    author = {Denny Zhou and Nathanael Scharli and Le Hou† Jason Wei and Nathan Scales and Xuezhi Wang and Dale Schuurmans† and Claire Cui and Olivier Bousquet and Quoc Le and Ed Chi},
    title = {LEAST-TO-MOST PROMPTING ENABLES COMPLEX REASONING IN LARGE LANGUAGE MODELS},
    booktitle = {Eleventh International Conference on Learning Representations},
    url = {https://arxiv.org/pdf/2205.10625.pdf},
    year = {2023}
}

@inproceedings{ai-chain,
author = {Wu, Tongshuang and Terry, Michael and Cai, Carrie Jun},
title = {AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts},
year = {2022},
isbn = {9781450391573},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3491102.3517582},
doi = {10.1145/3491102.3517582},
abstract = {Although large language models (LLMs) have demonstrated impressive potential on simple tasks, their breadth of scope, lack of transparency, and insufficient controllability can make them less effective when assisting humans on more complex tasks. In response, we introduce the concept of Chaining LLM steps together, where the output of one step becomes the input for the next, thus aggregating the gains per step. We first define a set of LLM primitive operations useful for Chain construction, then present an interactive system where users can modify these Chains, along with their intermediate results, in a modular way. In a 20-person user study, we found that Chaining not only improved the quality of task outcomes, but also significantly enhanced system transparency, controllability, and sense of collaboration. Additionally, we saw that users developed new ways of interacting with LLMs through Chains: they leveraged sub-tasks to calibrate model expectations, compared and contrasted alternative strategies by observing parallel downstream effects, and debugged unexpected model outputs by “unit-testing” sub-components of a Chain. In two case studies, we further explore how LLM Chains may be used in future applications.},
booktitle = {Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems},
articleno = {385},
numpages = {22},
keywords = {Natural Language Processing, Human-AI Interaction, Large Language Models},
location = {New Orleans, LA, USA},
series = {CHI '22}
}

@article{CTRL,
  author       = {Nitish Shirish Keskar and
                  Bryan McCann and
                  Lav R. Varshney and
                  Caiming Xiong and
                  Richard Socher},
  title        = {{CTRL:} {A} Conditional Transformer Language Model for Controllable
                  Generation},
  journal      = {Computing Research Repository},
  year         = {2019},
  url          = {http://arxiv.org/abs/1909.05858},
  eprinttype    = {arXiv},
  eprint       = {1909.05858},
}
@inproceedings{memory-augmented-gpt3,
    title = "Memory-assisted prompt editing to improve {GPT}-3 after deployment",
    author = "Madaan, Aman  and
      Tandon, Niket  and
      Clark, Peter  and
      Yang, Yiming",
    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.183",
    pages = "2833--2861",
    abstract = "Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans. For example, GPT-3 would mistakenly interpret {``}What word is similar to good?{''} to mean a homophone, while the user intended a synonym. Our goal is to effectively correct such errors via user interactions with the system but without retraining, which will be prohibitively costly. We pair GPT-3 with a growing memory of recorded cases where the model misunderstood the user{'}s intents, along with user feedback for clarification. Such a memory allows our system to produce enhanced prompts for any new query based on the user feedback for error correction on similar cases in the past. On four tasks (two lexical tasks, two advanced ethical reasoning tasks), we show how a (simulated) user can interactively teach a deployed GPT-3, substantially increasing its accuracy over the queries with different kinds of misunderstandings by the GPT-3. Our approach is a step towards the low-cost utility enhancement for very large pre-trained LMs.",
} % Retrieving via a dense store (memory) to improve prompt.

@ARTICLE{internet-augmentation-LLM,
    author = {{Lazaridou}, Angeliki and {Gribovskaya}, Elena and {Stokowiec}, Wojciech and {Grigorev}, Nikolai},
    title = "{Internet-augmented language models through few-shot prompting for open-domain question answering}",
    journal = {arXiv e-prints},
    year = 2022,
    month = mar,
    eid = {arXiv:2203.05115},
    pages = {arXiv:2203.05115},
    doi = {10.48550/arXiv.2203.05115},
    archivePrefix = {arXiv},
    eprint = {2203.05115},
    url = {https://ui.adsabs.harvard.edu/abs/2022arXiv220305115L},
}

@inproceedings{REALM,
    author = {Guu, Kelvin and Lee, Kenton and Tung, Zora and Pasupat, Panupong and Chang, Ming-Wei},
    title = {REALM: Retrieval-Augmented Language Model Pre-Training},
    year = {2020},
    booktitle = {Proceedings of the 37th International Conference on Machine Learning},
    articleno = {368},
    numpages = {10},
    series = {ICML'20},
    address = {Online},
}

@inproceedings{karimi-mahabadi-etal-2022-prompt,
    title = "Prompt-free and Efficient Few-shot Learning with Language Models",
    author = "Karimi Mahabadi, Rabeeh  and
      Zettlemoyer, Luke  and
      Henderson, James  and
      Mathias, Lambert  and
      Saeidi, Marzieh  and
      Stoyanov, Veselin  and
      Yazdani, Majid",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    
    url = "https://aclanthology.org/2022.acl-long.254",
    doi = "10.18653/v1/2022.acl-long.254",
    pages = "3638--3652",
    abstract = "Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM can score. In this work, we propose Perfect, a simple and efficient method for few-shot fine-tuning of PLMs without relying on any such handcrafting, which is highly effective given as few as 32 data points. Perfect makes two key design choices: First, we show that manually engineered task prompts can be replaced with task-specific adapters that enable sample-efficient fine-tuning and reduce memory and storage costs by roughly factors of 5 and 100, respectively. Second, instead of using handcrafted verbalizers, we learn new multi-token label embeddings during fine-tuning, which are not tied to the model vocabulary and which allow us to avoid complex auto-regressive decoding. These embeddings are not only learnable from limited data but also enable nearly 100x faster training and inference. Experiments on a wide range of few shot NLP tasks demonstrate that Perfect, while being simple and efficient, also outperforms existing state-of-the-art few-shot learning methods. Our code is publicly available at https://github.com/rabeehk/perfect.",
}

@inproceedings{NEURIPS2022_0cde695b,
    author = {Liu, Haokun and Tam, Derek and Muqeeth, Mohammed and Mohta, Jay and Huang, Tenghao and Bansal, Mohit and Raffel, Colin A},
    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 = {1950--1965},
    publisher = {Curran Associates, Inc.},
    title = {Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning},
    url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/0cde695b83bd186c1fd456302888454c-Paper-Conference.pdf},
    volume = {35},
    year = {2022}
}

@inproceedings{mauve,
    author = {Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
    booktitle = {Advances in Neural Information Processing Systems},
    editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
    pages = {4816--4828},
    publisher = {Curran Associates, Inc.},
    title = {MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
    url = {https://proceedings.neurips.cc/paper_files/paper/2021/file/260c2432a0eecc28ce03c10dadc078a4-Paper.pdf},
    volume = {34},
    year = {2021}
}

@inproceedings{zhao2019moverscore,
    title = {MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance},
    month = {August},
    year = {2019},
    author = {Wei Zhao and Maxime Peyrard and Fei Liu and Yang Gao and Christian M. Meyer and Steffen Eger},
    address = {Hong Kong, China},
    booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},
}

@inproceedings{flan-dataset,
    title={Finetuned Language Models are Zero-Shot Learners},
    author={Wei, Jason and Bosma, Maarten and Zhao, Vincent and Guu, Kelvin and Yu, Adams Wei and Lester, Brian and Du, Nan and Dai, Andrew M and Le, Quoc V},
    booktitle={International Conference on Learning Representations},
    year = {2021}
}

@article{t5-flan,
  author  = {Hyung Won Chung and Le Hou and Shayne Longpre and Barret Zoph and Yi Tay and William Fedus and Yunxuan Li and Xuezhi Wang and Mostafa Dehghani and Siddhartha Brahma and Albert Webson and Shixiang Shane Gu and Zhuyun Dai and Mirac Suzgun and Xinyun Chen and Aakanksha Chowdhery and Alex Castro-Ros and Marie Pellat and Kevin Robinson and Dasha Valter and Sharan Narang and Gaurav Mishra and Adams Yu and Vincent Zhao and Yanping Huang and Andrew Dai and Hongkun Yu and Slav Petrov and Ed H. Chi and Jeff Dean and Jacob Devlin and Adam Roberts and Denny Zhou and Quoc V. Le and Jason Wei},
  title   = {Scaling Instruction-Finetuned Language Models},
  journal = {Journal of Machine Learning Research},
  year    = {2024},
  volume  = {25},
  number  = {70},
  pages   = {1--53},
  url     = {http://jmlr.org/papers/v25/23-0870.html}
}

@inproceedings{hu2021lora,
  author       = {Edward J. Hu and
                  Yelong Shen and
                  Phillip Wallis and
                  Zeyuan Allen{-}Zhu and
                  Yuanzhi Li and
                  Shean Wang and
                  Weizhu Chen},
  title        = {LoRA: Low-Rank Adaptation of Large Language Models},
  journal      = {Computing Research Repository},
  booktitle = {Proceedings of the Tenth International Conference on Learning Representations},
  address = "Online",
  year         = {2021},
  url          = {https://arxiv.org/abs/2106.09685},
}

@inproceedings{redhot23,
    title = "{R}ed{HOT}: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media",
    author = "Wadhwa, Somin  and
      Khetan, Vivek  and
      Amir, Silvio  and
      Wallace, Byron",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    
    url = "https://aclanthology.org/2023.findings-eacl.61",
    pages = "809--827",
    abstract = "We present Reddit Health Online Talk (RedHOT), a corpus of 22,000 richly annotated social media posts from Reddit spanning 24 health conditions. Annotations include demarcations of spans corresponding to medical claims, personal experiences, and questions.We collect additional granular annotations on identified claims.Specifically, we mark snippets that describe patient Populations, Interventions, and Outcomes (PIO elements) within these. Using this corpus, we introduce the task of retrieving trustworthy evidence relevant to a given claim made on social media. We propose a new method to automatically derive (noisy) supervision for this task which we use to train a dense retrieval model; this outperforms baseline models. Manual evaluation of retrieval results performed by medical doctors indicate that while our system performance is promising, there is considerable room for improvement.We release all annotations collected (and scripts to assemble the dataset), and all code necessary to reproduce the results in this paper at: https://sominw.com/redhot.",
}

@inproceedings{Holtzman2020The,
    title={The Curious Case of Neural Text Degeneration},
    author={Ari Holtzman and Jan Buys and Li Du and Maxwell Forbes and Yejin Choi},
    booktitle={International Conference on Learning Representations},
    year={2020},
    url={https://openreview.net/forum?id=rygGQyrFvH}
}

@article{keskarCTRL2019,
    title={{CTRL - A Conditional Transformer Language Model for Controllable Generation}},
    author={Keskar, Nitish Shirish and McCann, Bryan and Varshney, Lav and Xiong, Caiming and Socher, Richard},
    journal={arXiv preprint arXiv:1909.05858},
    year={2019}
}

@article{chatgpt_askdocs,
    author = {Ayers, John W. and Poliak, Adam and Dredze, Mark and Leas, Eric C. and Zhu, Zechariah and Kelley, Jessica B. and Faix, Dennis J. and Goodman, Aaron M. and Longhurst, Christopher A. and Hogarth, Michael and Smith, Davey M.},
    title = "{Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum}",
    journal = {JAMA Internal Medicine},
    year = {2023},
    month = {04},
    issn = {2168-6106},
    doi = {10.1001/jamainternmed.2023.1838},
    url = {https://doi.org/10.1001/jamainternmed.2023.1838},
    eprint = {https://jamanetwork.com/journals/jamainternalmedicine/articlepdf/2804309/jamainternal\_ayers\_2023\_oi\_230030\_1683233221.03185.pdf},
}

@article{,
      title={LLaMA: Open and Efficient Foundation Language Models}, 
      author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
	   journal      = {Computing Research Repository},
      year={2023},
      eprint={2302.13971},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@ARTICLE{touvron2023llama,
       author = {{Touvron}, Hugo and {Lavril}, Thibaut and {Izacard}, Gautier and {Martinet}, Xavier and {Lachaux}, Marie-Anne and {Lacroix}, Timoth{\'e}e and {Rozi{\`e}re}, Baptiste and {Goyal}, Naman and {Hambro}, Eric and {Azhar}, Faisal and {Rodriguez}, Aurelien and {Joulin}, Armand and {Grave}, Edouard and {Lample}, Guillaume},
        title = "{{LLaMA}: {O}pen and {E}fficient {F}oundation {L}anguage {M}odels}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language},
         year = 2023,
        month = feb,
          eid = {arXiv:2302.13971},
        pages = {arXiv:2302.13971},
          doi = {10.48550/arXiv.2302.13971},
archivePrefix = {arXiv},
       eprint = {2302.13971},
 primaryClass = {cs.CL},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230213971T},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}



@misc{alpaca,
    author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
    title = {Stanford Alpaca: An Instruction-following LLaMA model},
    year = {2023},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}

@misc{alpaca_gpt4,
    author = {Chansung Park},
    title = {chansung/gpt4-alpaca-lora-7b},
    year = {2023},
    publisher = {HuggingFace},
    journal = {HuggingFace repository},
    howpublished = {\url{https://huggingface.co/chansung/gpt4-alpaca-lora-7b}},
}

@inproceedings{jin-etal-2019-pubmedqa,
    title = "{P}ub{M}ed{QA}: A Dataset for Biomedical Research Question Answering",
    author = "Jin, Qiao  and
      Dhingra, Bhuwan  and
      Liu, Zhengping  and
      Cohen, William  and
      Lu, Xinghua",
    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)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    
    url = "https://aclanthology.org/D19-1259",
    doi = "10.18653/v1/D19-1259",
    pages = "2567--2577",
    abstract = "We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts. PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances. Each PubMedQA instance is composed of (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. Our best performing model, multi-phase fine-tuning of BioBERT with long answer bag-of-word statistics as additional supervision, achieves 68.1{\%} accuracy, compared to single human performance of 78.0{\%} accuracy and majority-baseline of 55.2{\%} accuracy, leaving much room for improvement. PubMedQA is publicly available at https://pubmedqa.github.io.",
}

@inproceedings{lin-2004-rouge,
    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
    author = "Lin, Chin-Yew",
    booktitle = "Text Summarization Branches Out",
    month = jul,
    year = "2004",
    address = "Barcelona, Spain",
    
    url = "https://aclanthology.org/W04-1013",
    pages = "74--81",
}

@inproceedings{instructgpt,
 author = {Ouyang, Long and Wu, Jeffrey and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and Schulman, John and Hilton, Jacob and Kelton, Fraser and Miller, Luke and Simens, Maddie and Askell, Amanda and Welinder, Peter and Christiano, Paul F and Leike, Jan and Lowe, Ryan},
 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 = {27730--27744},
 publisher = {Curran Associates, Inc.},
 title = {Training language models to follow instructions with human feedback},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/b1efde53be364a73914f58805a001731-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}

@inproceedings{weinshall2018curriculum,
  title={Curriculum learning by transfer learning: Theory and experiments with deep networks},
  author={Weinshall, Daphna and Cohen, Gad and Amir, Dan},
  booktitle={International Conference on Machine Learning},
  pages={5238--5246},
  year={2018},
}

@inbook{rnn,
author = {Rumelhart, D. E. and Hinton, G. E. and Williams, R. J.},
title = {Learning Internal Representations by Error Propagation},
year = {1986},
isbn = {026268053X},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
booktitle = {Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations},
pages = {318–362},
numpages = {45}
}

@inproceedings{nctir,
  title={Overview of IR Tracks},
  author={Noriko Kando and Kazuko Kuriyama and Toshihiko Nozue and Koji Eguchi and Hiroyuki Kato and Soichiro Hidaka},
  booktitle = {Proceedings of the First NTCIR Workshop on Research in Japanese Text Retrieval and Term Recognition},
  year = {1999}
}

@inbook{splade,
author = {Formal, Thibault and Piwowarski, Benjamin and Clinchant, St\'{e}phane},
title = {SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking},
year = {2021},
isbn = {9781450380379},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3404835.3463098},
booktitle = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2288–2292},
numpages = {5}
}

@article{gpt2,
  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},
  journal = {OpenAI},
  year={2019},
  url={https://api.semanticscholar.org/CorpusID:160025533}
}

@ARTICLE{secnlp,
  title     = "{SECNLP}: A survey of embeddings in clinical natural language
               processing",
  author    = "Kalyan, Katikapalli Subramanyam and Sangeetha, S",
  abstract  = "Distributed vector representations or embeddings map variable
               length text to dense fixed length vectors as well as capture
               prior knowledge which can transferred to downstream tasks. Even
               though embeddings have become de facto standard for text
               representation in deep learning based NLP tasks in both general
               and clinical domains, there is no survey paper which presents a
               detailed review of embeddings in Clinical Natural Language
               Processing. In this survey paper, we discuss various medical
               corpora and their characteristics, medical codes and present a
               brief overview as well as comparison of popular embeddings
               models. We classify clinical embeddings and discuss each
               embedding type in detail. We discuss various evaluation methods
               followed by possible solutions to various challenges in clinical
               embeddings. Finally, we conclude with some of the future
               directions which will advance research in clinical embeddings.",
  journal   = "Journal of Biomedical Informatics",
  publisher = "Elsevier BV",
  volume    =  101,
  number    =  103323,
  pages     = "103323",
  month     =  jan,
  year      =  2020,
  keywords  = "Distributed representations; Embeddings; Medical; Natural
               language processing; Survey",
  copyright = "http://www.elsevier.com/open-access/userlicense/1.0/",
  language  = "en"
}

@article{Karimi2015,
  doi = {10.1016/j.jbi.2015.03.010},
  url = {https://doi.org/10.1016/j.jbi.2015.03.010},
  year = {2015},
  month = jun,
  publisher = {Elsevier {BV}},
  volume = {55},
  pages = {73--81},
  author = {Sarvnaz Karimi and Alejandro Metke-Jimenez and Madonna Kemp and Chen Wang},
  title = {Cadec: A corpus of adverse drug event annotations},
  journal = {Journal of Biomedical Informatics}
}
