Foundations of statistical natural language processing
Foundations of statistical natural language processing
The interaction of knowledge sources in word sense disambiguation
Computational Linguistics
A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Combining unsupervised lexical knowledge methods for word sense disambiguation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Sense information for disambiguation: confluence of supervised and unsupervised methods
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Neural Computation
Japanese word sense disambiguation using the simple bayes and support vector machine methods
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
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This paper proposes a method to improve the robustness of a word sense disambiguation (WSD) system for Japanese. Two WSD classifiers are trained from a word sense-tagged corpus: one is a classifier obtained by supervised learning, the other is a classifier using hypernyms extracted from definition sentences in a dictionary. The former will be suitable for the disambiguation of high frequency words, while the latter is appropriate for low frequency words. A robust WSD system will be constructed by combining these two classifiers. In our experiments, the F-measure and applicability of our proposed method were 3.4% and 10% greater, respectively, compared with a single classifier obtained by supervised learning.