Paper: Extracting Glosses to Disambiguate Word Senses

ACL ID N10-1088
Title Extracting Glosses to Disambiguate Word Senses
Venue Human Language Technologies
Session Main Conference
Year 2010

Like most natural language disambiguation tasks, word sense disambiguation (WSD) re- quires world knowledge for accurate predic- tions. Several proxies for this knowledge have been investigated, including labeled cor- pora, user-contributed knowledge, and ma- chine readable dictionaries, but each of these proxies requires significant manual effort to create, and they do not cover all of the ambigu- ous terms in a language. We investigate the task of automatically extracting world knowl- edge, in the form of glosses, from an unlabeled corpus. We demonstrate how to use these glosses to automatically label a training cor- pus to build a statistical WSD system that uses no manually-labeled data, with experimental results approaching that of a supervised SVM- based classifier.