@inproceedings{evalita2026overview,

title={EVALITA 2026: Overview of the 9th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian},

author={Cutugno, Francesco and Miaschi, Alessio and Aprosio, Alessio Palmero and Rambelli, Giulia and Siciliani, Lucia and Stranisci, Marco Antonio},

booktitle={Proceedings of the Ninth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2026)},

publisher = {CEUR.org},

year = {2026},

month = {February},

address = {Bari, Italy}

}

@inproceedings{raganato-etal-2023-semeval,
    title = "{S}em{E}val-2023 Task 1: Visual Word Sense Disambiguation",
    author = "Raganato, Alessandro  and
      Calixto, Iacer  and
      Ushio, Asahi  and
      Camacho-Collados, Jose  and
      Pilehvar, Mohammad Taher",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.308/",
    doi = "10.18653/v1/2023.semeval-1.308",
    pages = "2227--2234",
    abstract = "This paper presents the Visual Word Sense Disambiguation (Visual-WSD) task. The objective of Visual-WSD is to identify among a set of ten images the one that corresponds to the intended meaning of a given ambiguous word which is accompanied with minimal context. The task provides datasets for three different languages: English, Italian, and Farsi.We received a total of 96 different submissions. Out of these, 40 systems outperformed a strong zero-shot CLIP-based baseline. Participating systems proposed different zero- and few-shot approaches, often involving generative models and data augmentation. More information can be found on the task{'}s website: {\textbackslash}url{\{}https://raganato.github.io/vwsd/{\}}."
}

@inproceedings{navigli2010babelnet,
  title={BabelNet: Building a very large multilingual semantic network},
  author={Navigli, Roberto and Ponzetto, Simone Paolo},
  booktitle={Proceedings of the 48th annual meeting of the association for computational linguistics},
  pages={216--225},
  year={2010}
}

@article{miller1995wordnet,
  title={WordNet: a lexical database for English},
  author={Miller, George A},
  journal={Communications of the ACM},
  volume={38},
  number={11},
  pages={39--41},
  year={1995},
  publisher={ACM New York, NY, USA}
}

@inproceedings{kritharoula-etal-2023-large,
    title = "Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation",
    author = "Kritharoula, Anastasia  and
      Lymperaiou, Maria  and
      Stamou, Giorgos",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.807/",
    doi = "10.18653/v1/2023.emnlp-main.807",
    pages = "13053--13077",
    abstract = "Visual Word Sense Disambiguation (VWSD) is a novel challenging task with the goal of retrieving an image among a set of candidates, which better represents the meaning of an ambiguous word within a given context. In this paper, we make a substantial step towards unveiling this interesting task by applying a varying set of approaches. Since VWSD is primarily a text-image retrieval task, we explore the latest transformer-based methods for multimodal retrieval. Additionally, we utilize Large Language Models (LLMs) as knowledge bases to enhance the given phrases and resolve ambiguity related to the target word. We also study VWSD as a unimodal problem by converting to text-to-text and image-to-image retrieval, as well as question-answering (QA), to fully explore the capabilities of relevant models. To tap into the implicit knowledge of LLMs, we experiment with Chain-of-Thought (CoT) prompting to guide explainable answer generation. On top of all, we train a learn to rank (LTR) model in order to combine our different modules, achieving competitive ranking results. Extensive experiments on VWSD demonstrate valuable insights to effectively drive future directions."
}

@inproceedings{laba2024ukrainian,
  title={Ukrainian Visual Word Sense Disambiguation Benchmark},
  author={Laba, Yurii and Mohytych, Yaryna and Rohulia, Ivanna and Kyryleyza, Halyna and Dydyk-Meush, Hanna and Dobosevych, Oles and Hryniv, Rostyslav},
  booktitle={Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP)@ LREC-COLING 2024},
  pages={61--66},
  year={2024}
}

@inproceedings{kwon-etal-2023-vision,
    title = "Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information",
    author = "Kwon, Sunjae  and
      Garodia, Rishabh  and
      Lee, Minhwa  and
      Yang, Zhichao  and
      Yu, Hong",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.88/",
    doi = "10.18653/v1/2023.acl-long.88",
    pages = "1583--1598",
    abstract = "Visual Word Sense Disambiguation (VWSD) is a task to find the image that most accurately depicts the correct sense of the target word for the given context. Previously, image-text matching models often suffered from recognizing polysemous words. This paper introduces an unsupervised VWSD approach that uses gloss information of an external lexical knowledge-base, especially the sense definitions. Specifically, we suggest employing Bayesian inference to incorporate the sense definitions when sense information of the answer is not provided. In addition, to ameliorate the out-of-dictionary (OOD) issue, we propose a context-aware definition generation with GPT-3. Experimental results show that the VWSD performance significantly increased with our Bayesian inference-based approach. In addition, our context-aware definition generation achieved prominent performance improvement in OOD examples exhibiting better performance than the existing definition generation method."
}

@inproceedings{bevilacqua2021recent,
  title={Recent trends in word sense disambiguation: A survey},
  author={Bevilacqua, Michele and Pasini, Tommaso and Raganato, Alessandro and Navigli, Roberto},
  booktitle={International joint conference on artificial intelligence},
  pages={4330--4338},
  year={2021},
  organization={International Joint Conference on Artificial Intelligence, Inc}
}

@inproceedings{sumanathilaka2024assessing,
  title={Assessing GPT's Potential for Word Sense Disambiguation: A Quantitative Evaluation on Prompt Engineering Techniques},
  author={Sumanathilaka, Deshan and Micallef, Nicholas and Hough, Julian},
  booktitle={2024 IEEE 15th Control and System Graduate Research Colloquium (ICSGRC)},
  pages={204--209},
  year={2024},
  organization={IEEE}
}

@article{robinson2020contrastive,
  title={Contrastive learning with hard negative samples},
  author={Robinson, Joshua and Chuang, Ching-Yao and Sra, Suvrit and Jegelka, Stefanie},
  journal={arXiv preprint arXiv:2010.04592},
  year={2020}
}

@article{setitra2025leveraging,
  title={Leveraging ensemble deep models and llm for visual polysemy and word sense disambiguation},
  author={Setitra, Insaf and Rajapaksha, Praboda and Myat, Aung Kaung and Crespi, Noel},
  journal={Multimedia Tools and Applications},
  pages={1--33},
  year={2025},
  publisher={Springer}
}


@inproceedings{yang2024mta,
  title={MTA: A lightweight multilingual text alignment model for cross-language visual word sense disambiguation},
  author={Yang, Qihao and Wang, Xuelin and Li, Yong and Lee, Lap-Kei and Wang, Fu Lee and Hao, Tianyong},
  booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={12166--12170},
  year={2024},
  organization={IEEE}
}

@article{navigli2009word,
  title={Word sense disambiguation: A survey},
  author={Navigli, Roberto},
  journal={ACM computing surveys (CSUR)},
  volume={41},
  number={2},
  pages={1--69},
  year={2009},
  publisher={ACM New York, NY, USA}
}

@incollection{musacchio2025assessing,
  title={Assessing and Improving the Multilingual Visual Word Sense Disambiguation Ability of Vision-Language Models},
  author={Elio Musacchio and
          Lucia Siciliani and 
          Pierpaolo Basile and 
          Giovanni Semeraro},
  booktitle={ECAI 2025},
  pages={4145 - 4152},
  year={2025},
  publisher={IOS Press}
}

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "http://arxiv.org/abs/1908.10084",
}

@inproceedings{hromei2025grounded,
  title={Grounded Semantic Role Labelling from Synthetic Multimodal Data for Situated Robot Commands},
  author={Hromei, Claudiu Daniel and Scaiella, Antonio and Croce, Danilo and Basili, Roberto},
  booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  pages={23758--23781},
  year={2025}
}

@inproceedings{zhang2023srcb,
  title={SRCB at SemEval-2023 task 1: Prompt based and cross-modal retrieval enhanced visual word sense disambiguation},
  author={Zhang, Xudong and Zhen, Tiange and Zhang, Jing and Wang, Yujin and Liu, Song},
  booktitle={Proceedings of the 17th international workshop on semantic evaluation (SemEval-2023)},
  pages={439--446},
  year={2023}
}

@inproceedings{unitorev,
  title={UniTor at EVWSD-ITA: Zero-Shot Visual Word Sense
Disambiguation via Visual Question-Answering},
  author={Hromei, Claudiu Daniel and Scaiella, Antonio and Croce, Danilo and Basili, Roberto},
  booktitle = {Proceedings of the Ninth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2026)},
  publisher = {CEUR.org},
  year = {2026},
  month = {February},
  address = {Bari, Italy}
}

@inproceedings{hromei2025grounded,
  title={Grounded Semantic Role Labelling from Synthetic Multimodal Data for Situated Robot Commands},
  author={Hromei, Claudiu Daniel and Scaiella, Antonio and Croce, Danilo and Basili, Roberto},
  booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  pages={23758--23781},
  year={2025}
}

@inproceedings{pasini2021xl,
  title={XL-WSD: An extra-large and cross-lingual evaluation framework for word sense disambiguation},
  author={Pasini, Tommaso and Raganato, Alessandro and Navigli, Roberto},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={15},
  pages={13648--13656},
  year={2021}
}

@inproceedings{huang2019glossbert,
  title={GlossBERT: BERT for word sense disambiguation with gloss knowledge},
  author={Huang, Luyao and Sun, Chi and Qiu, Xipeng and Huang, Xuan-Jing},
  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)},
  pages={3509--3514},
  year={2019}
}

@article{yae2025leveraging,
  title={Leveraging large language models for word sense disambiguation},
  author={Yae, Jung H and Skelly, Nolan C and Ranly, Neil C and LaCasse, Phillip M},
  journal={Neural Computing and Applications},
  volume={37},
  number={6},
  pages={4093--4110},
  year={2025},
  publisher={Springer}
}

@inproceedings{meconi2025large,
  title={Do Large Language Models Understand Word Senses?},
  author={Meconi, Domenico and Stirpe, Simone and Martelli, Federico and Lavalle, Leonardo and Navigli, Roberto},
  booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  pages={33885--33904},
  year={2025}
}