An experiment in computational discrimination of English word senses
IBM Journal of Research and Development
Class-based n-gram models of natural language
Computational Linguistics
Two languages are more informative than one
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Contextual word similarity and estimation from sparse data
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th 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 disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Smoothing of automatically generated selectional constraints
HLT '93 Proceedings of the workshop on Human Language Technology
Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
The use of word sense disambiguation in an information extraction system
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Topical clustering of MRD senses based on information retrieval techniques
Computational Linguistics - Special issue on word sense disambiguation
Verb sense disambiguation based on dual distributional similarity
Natural Language Engineering
Word sense disambiguation in untagged text based on term weight learning
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
A concept-based adaptive approach to word sense disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
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
Prepositional phrase attachment through a hybrid disambiguation model
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
The research of word sense disambiguation method based on co-occurrence frequency of Hownet
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
Practical Word-Sense Disambiguation Using Co-occurring Concept Codes
Machine Translation
Using WordNet to Disambiguate Word Senses for Text Classification
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Bootstrapping distributional feature vector quality
Computational Linguistics
Deriving a multi-domain information extraction system from a rough ontology
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Conceptual information-based sense disambiguation
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Improving word sense disambiguation by pseudo-samples
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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Corpus-based sense disambiguation methods, like most other statistical NLP approaches, suffer from the problem of data sparseness. In this paper, we describe an approach which overcomes this problem using dictionary definitions. Using the definition-based conceptual co-occurrence data collected from the relatively small Brown corpus, our sense disambiguation system achieves an average accuracy comparable to human performance given the same contextual information.