A maximum entropy approach to natural language processing
Computational Linguistics
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
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
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Three generative, lexicalised models for statistical parsing
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
Using syntactic dependency as local context to resolve word sense ambiguity
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
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
Simple features for Chinese word sense disambiguation
COLING '02 Proceedings of the 19th international 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
English tasks: all-words and verb lexical sample
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Journal of Biomedical Informatics - Special issue: Building nursing knowledge through infomatics: from concept representation to data mining
Simple features for Chinese word sense disambiguation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A kernel PCA method for superior word sense disambiguation
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Semi-supervised training of a kernel PCA-based model for word sense disambiguation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Word sense disambiguation methods
Programming and Computing Software
VerbNet class assignment as a WSD task
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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In this paper we present a maximum entropy Word Sense Disambiguation system we developed which performs competitively on SENSEVAL-2 test data for English verbs. We demonstrate that using richer linguistic contextual features significantly improves tagging accuracy, and compare the system's performance with human annotator performance in light of both fine-grained and coarse-grained sense distinctions made by the sense inventory.