Large Scale Substitution-based Word Sense Induction

• URL: http://arxiv.org/abs/2110.07681v1
• Date: Thu, 14 Oct 2021 19:40:37 GMT
• Title: Large Scale Substitution-based Word Sense Induction
• Authors: Matan Eyal, Shoval Sadde, Hillel Taub-Tabib, Yoav Goldberg
• Abstract summary: We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a corpus-derived sense inventory and where each sense is associated with indicative words. Evaluation on English Wikipedia that was sense-tagged using our method shows that both the induced senses, and the per-instance sense assignment, are of high quality even compared to WSD methods, such as Babelfy.
• Score: 48.49573297876054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a corpus-derived sense inventory and where each sense is associated with indicative words. Evaluation on English Wikipedia that was sense-tagged using our method shows that both the induced senses, and the per-instance sense assignment, are of high quality even compared to WSD methods, such as Babelfy. Furthermore, by training a static word embeddings algorithm on the sense-tagged corpus, we obtain high-quality static senseful embeddings. These outperform existing senseful embeddings techniques on the WiC dataset and on a new outlier detection dataset we developed. The data driven nature of the algorithm allows to induce corpora-specific senses, which may not appear in standard sense inventories, as we demonstrate using a case study on the scientific domain.

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