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Exemplar-based word sense disambiguation: Some recent improvements (1997)
| Content Provider | CiteSeerX |
|---|---|
| Author | Ng, Hwee Tou |
| Description | 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. 1 |
| File Format | |
| Language | English |
| Publisher Date | 1997-01-01 |
| Publisher Institution | In Proceedings of the Second Conference on Empirical Methods in NLP |
| Access Restriction | Open |
| Subject Keyword | Naive-bayes Algorithm State-of-the-art Machine Improved Exemplar-based Classifier Data Set Recent Improvement Exemplar-based Learning Approach Exemplar-based Word Sense Disambiguation Nearest Neighbor Test Example Word Sense Disambiguation Large Sense-tagged Corpus 10-fold Cross Validation Disambiguation Accuracy |
| Content Type | Text |
| Resource Type | Article |
