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
Unsupervised sense disambiguation using bilingual probabilistic models
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Relieving the data acquisition bottleneck in word sense disambiguation
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Estimating class priors in domain adaptation for word sense disambiguation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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 equivalent pseudoword solution to Chinese word sense disambiguation
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
Aligning features with sense distinction dimensions
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
An unsupervised approach for bootstrapping Arabic sense tagging
Semitic '04 Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages
Machine learning with lexical features: the Duluth approach to Senseval-2
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
| Hi-index | 0.00 |
Word Sense Disambiguation (WSD) is usually considered to be a pattern classification to research and it has always being a key problem and one of difficult points in natural language processing. Statistical learning theory is a mainstream of the research method for WSD. The distribution of the word-senses of an ambiguous word is always not symmetrical and the distinction between word-senses' emergence frequency is great sometimes, so the judgment results are inclined to the maximum probability word-sense in the word-sense classification. The reflection of this phenomenon is obviously in the Bayesian model. When using the Bayesian model to carry on some research we find a new word-sense decision rule, which have a better precision than Bayesian model in WSD. In order to validate the credibility and stabilization of this method we carry through the experiment time and again, and acquire lots of experiment data. The results of the experiment indicate that new decision rule is more excellent than Bayesian decision rule. Furthermore this paper provides a theoretical foundation for this new decision rule.