The weighted majority algorithm
Information and Computation
Word sense disambiguation with pattern learning and automatic feature selection
Natural Language Engineering
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Supersense tagging of unknown nouns in WordNet
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Learning semantic classes for word sense disambiguation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Improving WSD with multi-level view of context monitored by similarity measure
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
A structural approach to the automatic adjudication of word sense disagreements
Natural Language Engineering
The noisy channel model for unsupervised word sense disambiguation
Computational Linguistics
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Learning word sense classes has been shown to be useful in fine-grained word sense disambiguation [Kohomban and Lee, 2005]. However, the common choice for sense classes, WordNet lexicographer files, are not designed for machine learning based word sense disambiguation. In this work, we explore the use of clustering techniques in an effort to construct sense classes that are more suitable for word sense disambiguation end-task. Our results show that these classes can significantly improve classifier performance over the state of the art results of unrestricted word sense disambiguation.