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 (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 algorithm, which was reported in (Mooney, 1996) to have the highest disambiguation accuracy among seven state-of-the-art machine learning algorithms.
Exemplar-Based Word Sense Disambiguation” Some Recent Improvements
Published 1997 in Conference on Empirical Methods in Natural Language Processing
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- Publication year
1997
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Conference on Empirical Methods in Natural Language Processing
- Publication date
1997-06-10
- Fields of study
Computer Science
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