Verifying person descriptions with term-entity association

Su Jian Li, Wenjie Li, Qin Lu, Rui Feng Xu

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


Person description extraction is an important task in biography generation, question answering and summarization, etc. While most of the previous extraction methods mainly depended on structural information, the work presented in the paper will focus on extraction verification by integrating linguistic knowledge provided by HowNet (with semantic knowledge) and the newswire corpus (with statistical information), from which the associations between terms (i.e. the words in HowNet) and person entities are measured. With Term-Entity association, ineligible descriptions extracted could be filtered out, and a higher precision is achieved in turn.
Original languageEnglish
Title of host publication2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Number of pages7
Publication statusPublished - 12 Dec 2005
EventInternational Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China
Duration: 18 Aug 200521 Aug 2005


ConferenceInternational Conference on Machine Learning and Cybernetics, ICMLC 2005


  • Description
  • Information extraction
  • Natural language processing
  • Term-entity association

ASJC Scopus subject areas

  • General Engineering


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