Word Sense Disambiguation

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13 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Modal Sense Disambiguation in Language Models: The Role of Context, Training Domain, and Input Frequency
Oct 19, 2026Ezgi Başar, Yevgen Matusevych, Annemarie van Dooren +1Word Sense Disambiguation Language Modeling
Center for Language and Cognition (CLCG), University of Groningen
WiC is Not WSD: A Study on LLMs and Lexical Ambiguity Resolution
Sep 17, 2026Yi Zhou, Kiamehr Rezaee, Danushka Bollegala +2Open-Weight Language Models Word Sense Disambiguation
Cardiff University · University of Liverpool · Amazon
Inspicio: Open-Vocabulary, LLM-Based Sense Retrieval for Historical Languages
Sep 1, 2026Michele CilettiLexical Retrieval Low-Resource Language Processing
Department of Humanities, University of Foggia / Via Arpi 176, 71121 Foggia, Italy
Divergent large language model predictions from convergent representations in ambiguous word pairs
Aug 3, 2026K. Jack Scott, Narun Pat, Veronica LiesaputraTransformer Interpretability Decoder-Only Language Models
Department of Psychology, University of Otago, Dunedin, New Zealand · School of Computing, University of Otago, Dunedin, New Zealand
When a Name Is Not a Name: A Benchmark Dataset and Distilled Reasoning for Culturally Entangled Bangla Homographs in Low-Resource LLMs
Jul 20, 2026Md. Asaduzzaman ShuvoCultural Bias in Language Models Language Model Bias Evaluation
United International University, Bangladesh
The BD-LSC Dataset: Facilitating the Benchmarking of Models for Lexical Semantic Change Detection in Slang and Standard Usage
Jun 15, 2026Afnan Aloraini, Riza Batista-NavarroSemantic Change Detection Word Sense Disambiguation
Department of Computer Science, The University of Manchester, Oxford Road, Manchester, M13 9PL, Greater Manchester, United Kingdom · Department of Computer Science, Qassim University, Al-Mulaida, 51425, Saudi Arabia
Sense Representations Are Inducible Interfaces
May 27, 2026Jan Christian Blaise Cruz, Alham Fikri AjiLanguage Model Steering Language Modeling
MBZUAI · SEACrowd
A new semantically annotated corpus with syntactic-semantic and cross-lingual senses
May 27, 2026Myriam Rakho, Eric Laporte, Matthieu ConstantWord Sense Disambiguation
Université Paris-Est, LIGM
RSD: Moving Local Triangular Charts for Auditing Language-Model Hidden States
May 17, 2026Seungmin JinWord Sense Disambiguation Hidden-State Probing
HSE University
SwanNLP at SemEval-2026 Task 5: An LLM-based Framework for Plausibility Scoring in Narrative Word Sense Disambiguation
Apr 17, 2026Deshan Sumanathilaka, Nicholas Micallef, Julian Hough +1LLM Evaluation Few-Shot Prompting
Department of Computer Science, Swansea University, Wales, UK
Polysemanticity or Polysemy? Lexical Identity Confounds Superposition Metrics
Apr 1, 2026Iyad Ait Hou, Rebecca HwaWord Sense Disambiguation Feature Superposition
Department of Computer Science George Washington University Washington, D.C, USA
Large language models can disambiguate opioid slang on social media
Mar 11, 2026Kristy A. Carpenter, Issah A. Samori, Mathew V. Kiang +4Word Sense Disambiguation Social Media Analysis
Departments of 1Biomedical Data Science, Stanford University, Stanford, CA 94305, USA · Departments of 2Bioengineering, Stanford University, Stanford, CA 94305, USA · Departments of 3Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA +5
An Exploration-Analysis-Disambiguation Reasoning Framework for Word Sense Disambiguation with Low-Parameter LLMs
Mar 5, 2026Deshan Sumanathilaka, Nicholas Micallef, Julian HoughWord Sense Disambiguation Small Language Models
Department of Computer Science, Swansea University, Fabian Way, Crymlyn Burrows, Swansea, Wales, UK

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Modal Sense Disambiguation in Language Models: The Role of Context, Training Domain, and Input Frequency

Ezgi Başar, Yevgen Matusevych, Annemarie van Dooren, Arianna Bisazza

Center for Language and Cognition (CLCG), University of Groningen

Word Sense Disambiguation Language Modeling LLM Evaluation

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Modals such as may and must can take on different senses depending on discourse context, alternating between root interpretations (e.g., permission, obligation) and epistemic interpretations (e.g., knowledge, belief). While modals are known to be notoriously difficult for children to acquire, limited research exists on whether language models (LMs) can reliably distinguish between different modal senses. We evaluate seven LMs that vary in architecture, size, and training data type using vector-based similarity and surprisal measures across four datasets. Specifically, we investigate whether LMs can leverage contextual information to disambiguate modal senses and whether their performance reflects the distribution of modal senses in their training data. We show that larger LMs trained on diverse text successfully leverage additional contextual information when available. In the absence of context, they tend to favor the dominant sense based on corpus statistics. Smaller developmentally plausible LMs, including those trained on child-directed language (CDL), fail to show consistent sensitivity to modal sense distinctions and do not benefit from context to the same degree.