Similarity-based methods for word sense disambiguation

Ido Dagan, Lillian Lee, Fernando Pereira

Research output: Contribution to journalConference articlepeer-review

104 Scopus citations

Abstract

We compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency. The similarity-based methods perform up to 40% better on this particular task. We also conclude that events that occur only once in the training set have major impact on similarity-based estimates.

Bibliographical note

Publisher Copyright:
© 1997 Proceedings of the Annual Meeting of the Association for Computational Linguistics. All Rights Reserved.

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