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An adaptive information retrieval system based on Neural Networks

  • Fabio Crestani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 686)

Abstract

This paper presents partial results of an experimental investigation concerning the use of Neural Networks in associative adaptive Information Retrieval. The learning and generalisation capabilities of the Backpropagation learning procedure are used to build up and employ application domain knowledge in the form of a sub-symbolic knowledge representation. The knowledge is acquired from examples of queries and relevant documents of the collection. In this paper the architecture of the system is presented and the results of the experimentation are briefly reported.

Keywords

Relevant Document Relevance Feedback Retrieval Performance Retrieval Phase Original 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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References

  1. [1]
    C. Claverdon, J. Mills, and M. Keen. ASLIB Cranfield Research Project: factors determining the performance of indexing systems. ASLIB, 1966.Google Scholar
  2. [2]
    Y. Miyata. A user's guide to PlaNet version 5.6: a tool for constructing, running and looking into a PDP network. Computer Science Department, University of Colorado, Boulder, USA, December 1990.Google Scholar
  3. [3]
    F. Crestani. A network model for Adaptive Information Retrieval. Departmental Research Report 1992/R6, Department of Computing Science, University of Glasgow, Glasgow, UK, April 1992.Google Scholar
  4. [4]
    C.J. van Rijsbergen. Information Retrieval. Second edition, Butterworths, London, 1979.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Fabio Crestani
    • 1
  1. 1.Dipartimento di Elettronica ed InformaticaUniversita' di PadovaItaly

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