In the LLM era, Word Sense Induction remains unsolved
In the absence of sense-annotated data, word sense induction (WSI) is a compelling alternative to word sense disambiguation, particularly in low-resource or domain-specific settings. In this paper, we emphasize methodological problems in current WSI evaluation. We propose an evaluation on a SemCor-derived dataset, respecting the original corpus polysemy and frequency distributions. We assess pre-trained embeddings and clustering algorithms across parts of speech, and propose and evaluate an LLM-based WSI method for English. We evaluate data augmentation sources (LLM-generated, corpus and lexicon), and semi-supervised scenarios using Wiktionary for data augmentation, must-link constraints,...
| Authors | Anna Mosolova, Marie Candito, Carlos Ramisch |
|---|---|
| Primary category | cs.CL |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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