Paper: Exemplar-Based Word Sense Disambiguation: Some Recent Improvements

ACL ID W97-0323
Title Exemplar-Based Word Sense Disambiguation: Some Recent Improvements
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 1997
Authors

In this paper, we report recent improve- ments to the exemplar-based learning ap- proach for word sense disambiguation that have achieved higher disambiguation accu- racy. By using a larger value of k, the number of nearest neighbors to use for de- termining the class of a test example, and through 10-fold cross validation to auto- matically determine the best k, we have ob- tained improved disambiguation accuracy on a large sense-tagged corpus first used in (Ng and Lee, 1996). The accuracy achieved by our improved exemplar-based classifier is comparable to the accuracy on the same data set obtained by the Naive-Bayes al- gorithm, which was reported in (Mooney, 1996) to have the highest disambiguation accuracy among seven state-of-the-art ma- chine learning algorithms.