Improving text similarity measurement by critical sentence vector model

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

doi:10.1007/11562382_44

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 proceeding › Conference article published in proceeding or book › Academic research › peer-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 language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	522-527
Number of pages	6
DOIs	https://doi.org/10.1007/11562382_44
Publication status	Published - 1 Dec 2005
Event	2nd Asia Information Retrieval Symposium, AIRS 2005 - Jeju Island, Korea, Republic of Duration: 13 Oct 2005 → 15 Oct 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3689 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd Asia Information Retrieval Symposium, AIRS 2005
Country/Territory	Korea, Republic of
City	Jeju Island
Period	13/10/05 → 15/10/05

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

More information

10.1007/11562382_44

Cite this

Li, W., Wong, K. F., Yuan, C., Li, W., & Xia, Y. (2005). Improving text similarity measurement by critical sentence vector model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 522-527). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3689 LNCS). https://doi.org/10.1007/11562382_44

Li, Wei ; Wong, Kam Fai ; Yuan, Chunfa et al. / Improving text similarity measurement by critical sentence vector model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. pp. 522-527 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{68cd4b0c62244d769d87e34f466450d0,

title = "Improving text similarity measurement by critical sentence vector model",

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.",

author = "Wei Li and Wong, {Kam Fai} and Chunfa Yuan and Wenjie Li and Yunqing Xia",

year = "2005",

month = dec,

day = "1",

doi = "10.1007/11562382_44",

language = "English",

isbn = "3540291865",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "522--527",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

note = "2nd Asia Information Retrieval Symposium, AIRS 2005 ; Conference date: 13-10-2005 Through 15-10-2005",

}

Li, W, Wong, KF, Yuan, C, Li, W & Xia, Y 2005, Improving text similarity measurement by critical sentence vector model. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3689 LNCS, pp. 522-527, 2nd Asia Information Retrieval Symposium, AIRS 2005, Jeju Island, Korea, Republic of, 13/10/05. https://doi.org/10.1007/11562382_44

Improving text similarity measurement by critical sentence vector model. / Li, Wei; Wong, Kam Fai; Yuan, Chunfa et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 522-527 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3689 LNCS).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

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AU - Li, Wei

AU - Wong, Kam Fai

AU - Yuan, Chunfa

AU - Li, Wenjie

AU - Xia, Yunqing

PY - 2005/12/1

Y1 - 2005/12/1

N2 - 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.

AB - 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.

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M3 - Conference article published in proceeding or book

SN - 3540291865

SN - 9783540291862

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 2nd Asia Information Retrieval Symposium, AIRS 2005

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Li W, Wong KF, Yuan C, Li W, Xia Y. Improving text similarity measurement by critical sentence vector model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 522-527. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11562382_44

Improving text similarity measurement by critical sentence vector model

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