A semi-supervised key phrase extraction approach: Learning from title phrases through a document semantic network

Decong Li, Sujian Li, Wenjie Li, Wei Wang, Weiguang Qu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

21 Citations (Scopus)


It is a fundamental and important task to extract key phrases from documents. Generally, phrases in a document are not independent in delivering the content of the document. In order to capture and make better use of their relationships in key phrase extraction, we suggest exploring the Wikipedia knowledge to model a document as a semantic network, where both n-ary and binary relationships among phrases are formulated. Based on a commonly accepted assumption that the title of a document is always elaborated to reflect the content of a document and consequently key phrases tend to have close semantics to the title, we propose a novel semi-supervised key phrase extraction approach in this paper by computing the phrase importance in the semantic network, through which the influence of title phrases is propagated to the other phrases iteratively. Experimental results demonstrate the remarkable performance of this approach.
Original languageEnglish
Title of host publicationACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Number of pages5
Publication statusPublished - 1 Dec 2010
Event48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 - Uppsala, Sweden
Duration: 11 Jul 201016 Jul 2010


Conference48th Annual Meeting of the Association for Computational Linguistics, ACL 2010

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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