  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.
