Exemplar-Based Word Sense Disambiguation: Some Recent Improvements
<|reference_start|>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 \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.<|reference_end|>
@article{ng1997exemplar-based,
    title={Exemplar-Based Word Sense Disambiguation: Some Recent Improvements},
    author={Hwee Tou Ng (DSO National Laboratories)},
    journal={In Proceedings of the Second Conference on Empirical Methods in
  Natural Language Processing (EMNLP-2), August 1997},
    year={1997},
    archivePrefix={arXiv},
    eprint={cmp-lg/9706010},
    primaryClass={cmp-lg cs.CL}
}