IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Parameter optimization for machine-learning of word sense disambiguation
Natural Language Engineering
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
Word-sense disambiguation using decomposable models
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
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
Modeling consensus: classifier combination for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Trajectory based word sense disambiguation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Data & Knowledge Engineering
Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion
Engineering Applications of Artificial Intelligence
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In this paper, we first argue that various ways of using context in WSD can be considered as distinct representations of a polysemous word under consideration, then all these representations are used jointly to identify the meaning of the target word. Under such a consideration, we can then straightforwardly apply the general framework for combining classifiers developed in Kittler et al. [5] to WSD problem. This results in many commonly used decision rules for WSD. The experimental result shows that the multi-representation based combination strategy of classifiers outperform individual ones as well as known techniques of classifier combination in WSD.