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An Information Retrieval Model Based on Latent Semantic Indexing with Intelligent Preprocessing

Author

Listed:
  • Ch. AswaniKumar

    (School of Computer Science, Vellore Institute of Technology, Deemed University, Vellore 632014, India)

  • Ankush Gupta

    (School of Computer Science, Vellore Institute of Technology, Deemed University, Vellore 632014, India)

  • Mahmooda Batool

    (School of Computer Science, Vellore Institute of Technology, Deemed University, Vellore 632014, India)

  • Shagun Trehan

    (School of Computer Science, Vellore Institute of Technology, Deemed University, Vellore 632014, India)

Abstract

The primary goal of an information retrieval system is to retrieve all the documents that are relevant to the user query. Disparities between the vocabulary of the system's authors and that of their users pose difficulties when information is processed without human intervention. Preprocessing the documents and user queries using intelligence techniques to remove the ambiguities in representation and indexing is the current area of research. In this paper, we present a novel intelligent method that has been appended to existing stemming and stopword removal processes. We designed an information retrieval system based on the proposed method using latent semantic indexing. The experimental results of the system using the proposed method exhibits the superiority over other systems based on traditional preprocessing methods.

Suggested Citation

  • Ch. AswaniKumar & Ankush Gupta & Mahmooda Batool & Shagun Trehan, 2005. "An Information Retrieval Model Based on Latent Semantic Indexing with Intelligent Preprocessing," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 4(04), pages 279-285.
    • Handle: RePEc:wsi:jikmxx:v:04:y:2005:i:04:n:s0219649205001250
    DOI: 10.1142/S0219649205001250
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    References listed on IDEAS

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    1. Catherine Kling, 1999. "Kopp, Raymond J., Werner W. Pommerehne, and Norbert Schwarz. Determining the Value of Non-Marketed Goods: Economic, Psychological, and Policy Relevant Aspects of Contingent Valuation Methods. Boston: ," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(2), pages 484-485.
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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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