%0 Journal Article
%T Exemplar-Based Word Sense Disambiguation: Some Recent Improvements
%A Hwee Tou Ng
%J Computer Science
%D 1997
%I arXiv
%X 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 \cite{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 \cite{mooney96} to have the highest disambiguation accuracy among seven state-of-the-art machine learning algorithms.
%U http://arxiv.org/abs/cmp-lg/9706010v1