An experiment in computational discrimination of English word senses
IBM Journal of Research and Development
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
A decision tree of bigrams is an accurate predictor of word sense
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Word sense disambiguation with distribution estimation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Word sense disambiguation is a crucial and difficult problem in natural language processing. The problem of word sense disambiguation can be considered as a typical classification problem. Different information is selected to build three classifiers based on Naïve Bayes. After forming the confusion matrix to show the ability of each classifier to each sense item as the pre-probability, we compose three classifiers to a multiple classifier system. The result of experiments shows that the multiple classifier system outperforms individual classifier.