Similarity-based methods for word sense disambiguation

  • Authors:
  • Ido Dagan;Lillian Lee;Fernando Pereira

  • Affiliations:
  • Bar Ilan University, Ramat Gan, Israel;Harvard University, Cambridge, MA;AT&T Labs-Research, Murray Hill, NJ

  • Venue:
  • ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
  • Year:
  • 1997

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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.