Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
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
Unsupervised discrimination and labeling of ambiguous names
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Name discrimination by clustering similar contexts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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SenseClusters is a freely available intelligent system that clusters together similar contexts in natural language text. Thereafter it assigns identifying labels to these clusters based on their content. It is a purely unsupervised approach that is language independent, and uses no knowledge other than what is available in raw un-annotated corpora. In addition to clustering similar contexts, it can be used to identify synonyms and sets of related words. It has been applied to a diverse range of problems, including proper name disambiguation, word sense discrimination, email organization, and document clustering. SenseClusters is a complete system that supports feature selection from large corpora, several different context representation schemes, various clustering algorithms, the creation of descriptive and discriminating labels for the discovered clusters, and evaluation relative to gold standard data.