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Optimising the Heuristics in Latent Semantic Indexing for Effective Information Retrieval

Author

Listed:
  • 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)

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.

Suggested Citation

  • S. Srinivas & Ch. AswaniKumar, 2006. "Optimising the Heuristics in Latent Semantic Indexing for Effective Information Retrieval," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 5(02), pages 97-105.
    • Handle: RePEc:wsi:jikmxx:v:05:y:2006:i:02:n:s0219649206001359
    DOI: 10.1142/S0219649206001359
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    Cited by:

    1. Saida Ishak Boushaki & Nadjet Kamel & Omar Bendjeghaba, 2018. "High-Dimensional Text Datasets Clustering Algorithm Based on Cuckoo Search and Latent Semantic Indexing," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 1-24, September.

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