Keyphrase extraction based on topic relevance and term association

Decong Li, Sujian Li, Wenjie Li, Congyun Gu, Yun Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Keyphrases are concise representation of documents and usually are extracted directly from the original text. This paper proposes a novel approach to extract keyphrases. This method proposes two metrics, named topic relevance and term association respectively, for determining whether a term is a keyphrase. Using Wikipedia knowledge and betweenness computation, we compute these two metrics and combine them to extract important phrases from the text. Experimental results show the effectiveness of the proposed approach for keyphrases extaction.
Original languageEnglish
Pages (from-to)293-299
Number of pages7
JournalJournal of Information and Computational Science
Volume7
Issue number1
Publication statusPublished - 1 Jan 2010

Keywords

  • Betweenness
  • Keyphrase extraction
  • Term association
  • Topic relevance

ASJC Scopus subject areas

  • Information Systems
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics
  • Library and Information Sciences

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