Density Maximization of Context-to-sense Mapping for Unsupervised Word Sense Disambiguation

This paper proposes a novel unsupervised method employing large amount of unlabeled text corpora for all-words word sense disambiguation (WSD), which requires to discriminate huge variety of senses, thus unsupervised methods are desired to avoid constructing costly sense-labeled corpora. Given unlabeled corpora and a dictionary, the proposed method bases on the coherent correspondences between word contexts and word senses, and finds the all-words’ senses that maximize mapping density in context-to-sense product metric space. Experimental results confirmed the efficacy of our unsupervised method by showing the reliability of disambiguation if sufficient variations of word-types are provided in similar context.

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