Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on 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
Discriminating among word senses using McQuitty's similarity analysis
NAACLstudent '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Proceedings of the HLT-NAACL 2003 student research workshop - Volume 3
Using a semantic concordance for sense identification
HLT '94 Proceedings of the workshop on Human Language Technology
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Many approach strategies can be employed to resolve word sense ambiguity with a reasonable degree of accuracy. These strategies are: knowledge-based, corpus-based, and hybrid-based. This paper pays attention to the corpus-based strategy that employs an unsupervised learning method for disambiguation. We report our investigation of Latent Semantic Indexing (LSI), an unsupervised learning, to the task of Thai noun and verbal word sense disambiguation. We report experiments on two Thai polysemous words, namely Unknown XML node MediaObject /hua4/ and Unknown XML node MediaObject /kep1/ that are used as a representative of Thai nouns and verbs respectively. The results of these experiments demonstrate the effectiveness and indicate the potential of applying vector-based distributional information measures to semantic disambiguation. Our approach performs better than a baseline system, which picks the most frequent sense.