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.

Paper formats: ps, pdf, other

Data: http://www.cs.cornell.edu/home/llee/data/sim.html

BibTeX entry:

@inproceedings{Dagan+Lee+Pereira:comp,
  author =	 "Ido Dagan and Lillian Lee and Fernando Pereira",
  title =	 "Similarity-Based Methods for Word Sense Disambiguation",
  booktitle = 	 "35th Annual Meeting of the ACL",
  year = 	 1997,
  pages =	 {56--63}
}