Exemplar-Based Word Sense Disambiguation: Some Recent Improvements

In this paper, we report recent improvements to the exemplar-based learning approach for word sense disambiguation that have achieved higher disambiguation accuracy. By using a larger value of k, the number of nearest neighbors to use for determining the class of a test example, and through 10-fold cross validation to automatically determine the best k, we have obtained improved disambiguation accuracy on a large sense-tagged corpus first used in ng96. The accuracy achieved by our improved exemplar-based classifier is comparable to the accuracy on the same data set obtained by the Naive-Bayes algorithm, which was reported in mooney96 to have the highest disambiguation accuracy among seven state-of-the-art machine learning algorithms.