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.
@inproceedings{Dagan+Lee+Pereira:comp, author = {Ido Dagan and Lillian Lee and Fernando Pereira}, title = {Similarity-based methods for word sense disambiguation}, year = {1997}, pages = {56--63}, booktitle = {Proceedings of the 35th ACL/8th EACL} }