Leveraging Large Language Models for Word Sense Disambiguation
Document Type
Article
Publication Date
12-19-2024
Abstract
Natural language processing (NLP) is difficult because human language contains ambiguity. The same word can have a different meaning depending on the context and may result in different interpretations given biases held by a NLP technique. Correctly interpreting this ambiguity is not simply an important task in its own right but is a key enabler to major NLP activities such as machine translation and question answering. This research proposes three techniques to evaluate a large language models’(LLMs) ability to perform word sense disambiguation (WSD) and explores the efficacy of seven generative LLMs. The first technique assesses whether LLMs can, given a context sentence, select the correct word sense from a menu of options. The second asks LLMs, without options provided, to state whether or not a provided word sense is correct. The third technique presents the LLMs with context and an unseen word, assessing whether the LLMs can infer from context the sense of a word that it has not seen during training. Results demonstrate a strong relationship between model size and performance. Applications of WSD are demonstrated as part of an information extraction pipelines supporting sentiment analysis and as part of an LLM-evaluation suite to support machine learning operations.
Source Publication
Neural Computing and Applications
Recommended Citation
Yae, J. H., Skelly, N. C., Ranly, N. C., & LaCasse, P. M. (2025). Leveraging large language models for word sense disambiguation. Neural Computing and Applications, 37:4093-4110. https://doi.org/10.1007/s00521-024-10747-5
Comments
The license for this article changed 31 January 2025. This change was issued through a correction posted to the journal at https://doi.org/10.1007/s00521-025-11082-z
© The Author(s) 2024
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