Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Estimating upper and lower bounds on the performance of word-sense disambiguation programs
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Word translation disambiguation using Bilingual Bootstrapping
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Meaningful clustering of senses helps boost word sense disambiguation performance
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
An empirical study of the behavior of active learning for word sense disambiguation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
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This paper presents a new approach based on Vicarious Words (VWs) to resolve Word Sense Discrimination (WSD) in Chinese language. VWs are particular artificial ambiguous words, which can be used to realize unsupervised WSD. A Bayesian classifier is implemented to test the efficacy of the VW solution on Senseval-3 Chinese test suite. The performance is better than state-of-the-art results with an average F-measure of 0.80. The experiment verifies the value of VW for unsupervised method in WSD.