The interaction of knowledge sources in word sense disambiguation
Computational Linguistics
To what extent does case contribute to verb sense disambiguation?
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
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
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
Combining heterogeneous classifiers for word-sense disambiguation
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
JAIST: Clustering and classification based approaches for Japanese WSD
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
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This paper proposes a robust method for word sense disambiguation (WSD) of Japanese. Four classifiers were combined in order to improve recall and applicability: one used example sentences in a machine readable dictionary (MRD), one used grammatical information in an MRD, and two classifiers were obtained by supervised learning from a sense-tagged corpus. In other words, we combined several classifiers using heterogeneous language resources, an MRD and a word sense tagged corpus. According to our experimental results, the proposed method outperformed the best single classifier for recall and applicability.