Lexical ambiguity and information retrieval
ACM Transactions on Information Systems (TOIS)
An empirical symbolic approach to natural language processing
Artificial Intelligence - Special volume on empirical methods
Integrating general-purpose and corpus-based verb classification
Computational Linguistics
Computational lexicons: the neat examples and the odd exemplars
ANLC '92 Proceedings of the third conference on Applied natural language processing
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Similarity-based estimation of word cooccurrence probabilities
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Word sense ambiguation: clustering related senses
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Generalizing automatically generated selectional patterns
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
A rule-based and MT-oriented approach to prepositional phrase attachment
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Generalizing case frames using a thesaurus and the MDL principle
Computational Linguistics
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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It is widely accepted that tagging text with semantic information would improve the quality of lexical learning in corpus-based NLP methods. However available on-line taxonomies are rather entangled and introduce an unnecessary level of ambiguity. The noise produced by the redundant number of tags often overrides the advantage of semantic tagging. In this paper we propose an automatic method to select from WordNet a subset of domain-appropriate categories that effectively reduce the overambiguity of WordNet, and help at identifying and categorise relevant language patterns in a more compact way. The method is evaluated against a manually tagged corpus, SEMCOR.