Current vector-space models of lexical semantics create a single “prototype” vector to represent the meaning of a word. However, due to lexical ambiguity, encoding word meaning with a single vector is problematic. This paper presents a method that uses clustering to produce multiple “sense-specific&rdquo vectors for each word. This approach provides a context-dependent vector representation of word meaning that naturally accommodates homonymy and polysemy. Experimental comparisons to human judgements of semantic similarity for both isolated words as well as words in sentential contexts demonstrate the superiority of this approach over both prototype and exemplar based vector-space models.

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In Proceedings of the 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2010), pp. 109-117 2010.

@inproceedings{reisinger:naacl10, title={Multi-Prototype Vector-Space Models of Word Meaning}, author={Joseph Reisinger and Raymond J. Mooney}, booktitle={Proceedings of the 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2010)}, pages={109-117}, url="http://www.cs.utexas.edu/users/ai-lab?reisinger:naacl10", year={2010} }

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