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SIGIR ’94 pp 282–291Cite as

Improving Text Retrieval for the Routing Problem using Latent Semantic Indexing

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Abstract

Latent Semantic Indexing (LSI) is a novel approach to information retrieval that attempts to model the underlying structure of term associations by transforming the traditional representation of documents as vectors of weighted term frequencies to a new coordinate space where both documents and terms are represented as linear combinations of underlying semantic factors. In previous research, LSI has produced a small improvement in retrieval performance. In this paper, we apply LSI to the routing task, which operates under the assumption that a sample of relevant and non-relevant documents is available to use in constructing the query. Once again, LSI slightly improves performance. However, when LSI is used is conjuction with statistical classification, there is a dramatic improvement in performance.

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Author information

Authors and Affiliations

  1. Xerox PARC and Stanford University, 3333 Coyote Hill Rd., Palo Alto, CA, 94304, USA

    David Hull

Authors
  1. David Hull
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Massachusetts, 01003, Amherst, MA, USA

    Bruce W. Croft

  2. Department of Computer Science, University of Glasgow, G12 8RZ, 8–17 Lilybank Gardens, Glasgow, Scotland

    C. J. van Rijsbergen

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© 1994 Springer-Verlag London Limited

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Hull, D. (1994). Improving Text Retrieval for the Routing Problem using Latent Semantic Indexing. In: Croft, B.W., van Rijsbergen, C.J. (eds) SIGIR ’94. Springer, London. https://doi.org/10.1007/978-1-4471-2099-5_29

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  • DOI: https://doi.org/10.1007/978-1-4471-2099-5_29

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19889-5

  • Online ISBN: 978-1-4471-2099-5

  • eBook Packages: Springer Book Archive

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