One sense per context cluster: Improving word sense disambiguation using web-scale phrase clustering

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The performance of word sense disambiguation task is still limited by lexical context matching due to data sparse problem. In this paper we present a simple but effective method that incorporates web-scale phrase clustering results for context matching. This method is able to capture some semantic relations that are not in WordNet. Without using any additional labeled data this new approach obtained 2.11%-6.92% higher accuracy over a typical supervised classifier.

Original languageEnglish (US)
Title of host publication2010 4th International Universal Communication Symposium, IUCS 2010 - Proceedings
Pages181-184
Number of pages4
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event2010 4th International Universal Communication Symposium, IUCS 2010 - Beijing, China
Duration: Oct 18 2010Oct 19 2010

Publication series

Name2010 4th International Universal Communication Symposium, IUCS 2010 - Proceedings

Conference

Conference2010 4th International Universal Communication Symposium, IUCS 2010
CountryChina
CityBeijing
Period10/18/1010/19/10

Keywords

  • Clustering
  • Web-scale N-grams
  • Word sense disambiguation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Communication

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    Ji, H. (2010). One sense per context cluster: Improving word sense disambiguation using web-scale phrase clustering. In 2010 4th International Universal Communication Symposium, IUCS 2010 - Proceedings (pp. 181-184). [5666225] (2010 4th International Universal Communication Symposium, IUCS 2010 - Proceedings). https://doi.org/10.1109/IUCS.2010.5666225