Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
HLT '91 Proceedings of the workshop on Speech and Natural Language
Differentiating homonymy and polysemy in information retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Regular polysemy: a distributional model
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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We consider the problem of distinguishing polysemous from homonymous nouns. This distinction is often taken for granted, but is seldom operationalized in the shape of an empirical model. We present a first step towards such a model, based on WordNet augmented with ontological classes provided by CoreLex. This model provides a polysemy index for each noun which (a), accurately distinguishes between polysemy and homonymy; (b), supports the analysis that polysemy can be grounded in the frequency of the meaning shifts shown by nouns; and (c), improves a regression model that predicts when the "one-sense-per-discourse" hypothesis fails.