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Connectionist and Genetic Approaches for Information Retrieval

  • Mohand Boughanem
  • Claude Chrisment
  • Josiane Mothe
  • Chantal Soule-Dupuy
  • Lynda Tamine
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 50)

Abstract

In the past few decades, knowledge based techniques have made an impressive contribution to intelligent information retrieval (IR). These techniques stem from research on artificial intelligence, neural networks (NN) and genetic algorithms (GA) and are used to answer three main IR tasks: information modelling, query evaluation and relevance feedback. The paper describes IR approaches based on connectionist and genetic approaches. Our goal is to take benefits of these techniques to fulfill the user information needs. More precisely a multi-layer NN, Mercure, is used to represent the document space in an associative way, to evaluate the query using spreading activation and to implement a relevance feedback process by relevance back-propagation. Another query reformulation technique is investigated which uses the GA approach. The GA generates several queries that explore different areas of the document space. Experiments and results obtained with both techniques are shown and discussed.

Keywords

Information Retrieval Relevant Document Relevance Feedback Spreading Activation Initial Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Mohand Boughanem
    • 1
  • Claude Chrisment
    • 1
  • Josiane Mothe
    • 1
  • Chantal Soule-Dupuy
    • 1
  • Lynda Tamine
    • 1
  1. 1.Institut de Recherche en Informatique de ToulouseToulouse Cedex 4France

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