Unsupervised Word Sense Induction from Multiple Semantic Spaces with Locality Sensitive Hashing

Word Sense Disambiguation is the task dedicated to the problem of finding out the sense of a word in context, from all of its many possible senses. Solving this problem requires to know the set of possible senses for a given word, which can be acquired from human knowledge, or from automatic discovery, called Word Sense Induction. In this article, we adapt two existing meta-methods of Word Sense Induction for the automatic construction of a disambiguation lexicon. Our adaptation is based on multiple semantic spaces (also called Word Space Models) produced from a syntactic analysis of a very large number of web pages. These adaptations and the results presented in this article dier from the original methods in that they use a combination of several high dimensional spaces instead of one single representation. Each of these competing semantic spaces takes part in a clustering phase in which they vote on sense induction.

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