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A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment
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A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment
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A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment
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Journal Article
A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment
2015
Overview
Word sense induction (WSI) seeks to automatically discover the senses of a word
in a corpus via unsupervised methods. We propose a sense-topic model for WSI,
which treats sense and topic as two separate latent variables to be inferred
jointly. Topics are informed by the entire document, while senses are informed
by the local context surrounding the ambiguous word. We also discuss
unsupervised ways of enriching the original corpus in order to improve model
performance, including using neural word embeddings and external corpora to
expand the context of each data instance. We demonstrate significant
improvements over the previous state-of-the-art, achieving the best results
reported to date on the SemEval-2013 WSI task.
Publisher
MIT Press,MIT Press Journals, The,The MIT Press
