Improving Text Similarity Measurement by Critical Sentence Vector Model

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 me...

Full description

Saved in:
Bibliographic Details
Published inInformation Retrieval Technology pp. 522 - 527
Main Authors Li, Wei, Wong, Kam-Fai, Yuan, Chunfa, Li, Wenjie, Xia, Yunqing
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
Edition1ère éd
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Combine Method
Common Noun
Computer science; control theory; systems
Exact sciences and technology
Information systems. Data bases
Memory organisation. Data processing
Proper Noun
Semantic Information
Software
Weight Assignment
Vector space
Keyword
Similarity
Semantics
Document structure
Information retrieval
Metric
Text
Vector method
Modeling
Sentence
Online AccessGet full text

Cover

More Information
Summary: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.
ISBN:9783540291862
3540291865
ISSN:0302-9743
1611-3349
DOI:10.1007/11562382_44