Measuring term informativeness in context

Zhaohui Wu, C. Lee Giles

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

21 Scopus citations

Abstract

Measuring term informativeness is a fundamental NLP task. Existing methods, mostly based on statistical information in corpora, do not actually measure informativeness of a term with regard to its semantic context. This paper proposes a new lightweight feature-free approach to encode term informativeness in context by leveraging web knowledge. Given a term and its context, we model contextaware term informativeness based on semantic similarity between the context and the term's most featured context in a knowledge base, Wikipedia. We apply our method to three applications: core term extraction from snippets (text segment), scientific keywords extraction (paper), and back-of-The-book index generation (book). The performance is state-of-Theart or close to it for each application, demonstrating its effectiveness and generality.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies
PublisherAssociation for Computational Linguistics (ACL)
Pages259-269
Number of pages11
ISBN (Electronic)9781937284473
StatePublished - 2013
Event2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2013 - Atlanta, United States
Duration: Jun 9 2013Jun 14 2013

Publication series

NameNAACL HLT 2013 - 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Main Conference

Other

Other2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2013
Country/TerritoryUnited States
CityAtlanta
Period6/9/136/14/13

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Computer Science Applications
  • Linguistics and Language

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