Improving text similarity measurement by critical sentence vector model

Wei Li, Kam Fai Wong, Chunfa Yuan, Wenjie Li, Yunqing Xia

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

2 Citations (Scopus)

Abstract

We propose the Critical Sentence Vector Model (CSVM), a novel model to measure text similarity. The CSVM accounts for the structural and semantic information of the document. Compared to existing methods based on keyword vector, e.g. Vector Space Model (VSM), CSVM measures documents similarity by measuring similarity between critical sentence vectors extracted from documents. Experiments show that CSVM outperforms VSM in calculation of text similarity.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages522-527
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2005
Event2nd Asia Information Retrieval Symposium, AIRS 2005 - Jeju Island, Korea, Republic of
Duration: 13 Oct 200515 Oct 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3689 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd Asia Information Retrieval Symposium, AIRS 2005
CountryKorea, Republic of
CityJeju Island
Period13/10/0515/10/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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