Optimising the Heuristics in Latent Semantic Indexing for Effective Information Retrieval

S. Srinivas and Ch. AswaniKumar ()
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S. Srinivas: School of Science and Humanities, Vellore Institute of Technology, Deemed University, Vellore, India
Ch. AswaniKumar: School of Computing Sciences, Vellore Institute of Technology, Deemed University, Vellore, India

Journal of Information & Knowledge Management (JIKM), 2006, vol. 05, issue 02, 97-105

Abstract: Latent Semantic Indexing (LSI) is a famous Information Retrieval (IR) technique that tries to overcome the problems of lexical matching using conceptual indexing. LSI is a variant of vector space model and proved to be 30% more effective. Many studies have reported that good retrieval performance is related to the use of various retrieval heuristics. In this paper, we focus on optimising two LSI retrieval heuristics: term weighting and rank approximation. The results obtained demonstrate that the LSI performance improves significantly with the combination of optimised term weighting and rank approximation.

Keywords: Information retrieval; Latent Semantic Indexing; rank approximation; term weighting; vector space method (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219649206001359

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