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New Technology of Library and Information Service  2001, Vol. 17 Issue (5): 40-41    DOI: 10.11925/infotech.1003-3513.2001.05.13
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Natural Language Retrieval for Latent Semantic Indexing
Tao Yuehua  Sun Maosong2
1(Department of Computer Science, Yunnan Normal University, Kunming 650031, China)
2(Department of Computer Science and Technology; Tsinghua University, Beijing 10084, China)
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In information retrieval, vector space model is one of significant mathematics tools. Because of the speciality of natural language retrieval (NLR) and the affection of synonym and polysemy in tradition information retrieval model, the precision of retrieval is not high. In order to imporve the precision of NLR, the authors discuss information retrieval model based on concept--Latent Semantic Indexing (LSI), and analysis two examples of LSI.

Key wordsNatural language      Information retrieval      Latent Semantic      Indexing     
Received: 30 December 2000      Published: 25 October 2001


Corresponding Authors: Tao Yuehua,Sun Maosong   
About author:: Tao Yuehua,Sun Maosong

Cite this article:

Tao Yuehua,Sun Maosong. Natural Language Retrieval for Latent Semantic Indexing. New Technology of Library and Information Service, 2001, 17(5): 40-41.

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[1]Jennifer Chu-Carroll&Bob Carpenter.1999.Vector-based Natural Language Call Routing.Computational Linguistics,25

[2]Michael W.Berry and Murray Browne.Understanding Search Engines Mathematical Modeling and Text Retrieval.University
of Tennessee.USA

[3]Deer wester,Scott,Susan T.Dumais,George W.Furnas,Thomas K.Landauer,and Richard Harshman.1990.Indexing
by Latent Semantic Analysis.Journal of the American Society for Information Science,41(6):391-407.


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