Similarity-based methods for word sense disambiguation

I. Dagan, Lillian Lee, Fernando Pereira

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.
Original languageAmerican English
Title of host publicationThe 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
PublisherAssociation for Computational Linguistics
StatePublished - 1997

Bibliographical note

Place of conference:Madrid, Spain


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