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Soft Computing in Information Retrieval

Techniques and Applications

  • Book
  • © 2000

Overview

Editors:
  1. Fabio Crestani

    1. Department of Computing Science, University of Glasgow, Glasgow, Scotland

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    You can also search for this editor in PubMed   Google Scholar

  2. Gabriella Pasi

    1. ITIM-CNR, Milano, Italy

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 50)

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-xii
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  3. Neural Networks

    1. Front Matter

      Pages 75-75
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    2. Visual Keywords: from Text Retrieval to Multimedia Retrieval

      • Joo-Hwee Lim
      Pages 77-101

    3. Document Classification with Unsupervised Artificial Neural Networks

      • Dieter Merkl, Andreas Rauber
      Pages 102-121

    4. The Java Search Agent Workshop

      • Hsinchun Chen, Marshall Ramsey, Po Li
      Pages 122-140

    5. A Connectionist Approach to Content Access in Documents: Application to Detection of Jokes

      • Stephane Zrehen
      Pages 141-169

  4. Genetic Algorithms

    1. Front Matter

      Pages 171-171
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    2. Connectionist and Genetic Approaches for Information Retrieval

      • Mohand Boughanem, Claude Chrisment, Josiane Mothe, Chantal Soule-Dupuy, Lynda Tamine
      Pages 173-198

    3. Large Population or Many Generations for Genetic Algorithms? Implications in Information Retrieval

      • Dana Vrajitoru
      Pages 199-222

  5. Evidential and Probabilistic Reasoning

    1. Front Matter

      Pages 223-223
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    2. A Logical Information Retrieval Model Based on a Combination of Propositional Logic and Probability Theory

      • Justin Picard, Jacques Savoy
      Pages 225-258

    3. Bayesian Network Models for Information Retrieval

      • Berthier Ribeiro-Neto, Ilmério Silva, Richard Muntz
      Pages 259-291

    4. Probabilistic Learning by Uncertainty Sampling with Non-Binary Relevance

      • Gianni Amati, Fabio Crestani
      Pages 292-313

  6. Rough Sets Theory, Multivalued Logics, and Other Approaches

    1. Front Matter

      Pages 315-315
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    2. Granular Information Retrieval

      • S. K. Michael Wong, Y. Y. Yao, Cory J. Butz
      Pages 317-331

    3. A Framework for the Retrieval of Multimedia Objects Based on Four-Valued Fuzzy Description Logics

      • Umberto Straccia
      Pages 332-357
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Keywords

About this book

Information retrieval (IR) aims at defining systems able to provide a fast and effective content-based access to a large amount of stored information. The aim of an IR system is to estimate the relevance of documents to users' information needs, expressed by means of a query. This is a very difficult and complex task, since it is pervaded with imprecision and uncertainty. Most of the existing IR systems offer a very simple model of IR, which privileges efficiency at the expense of effectiveness. A promising direction to increase the effectiveness of IR is to model the concept of "partially intrinsic" in the IR process and to make the systems adaptive, i.e. able to "learn" the user's concept of relevance. To this aim, the application of soft computing techniques can be of help to obtain greater flexibility in IR systems.

Editors and Affiliations

  • Department of Computing Science, University of Glasgow, Glasgow, Scotland

    Fabio Crestani

  • ITIM-CNR, Milano, Italy

    Gabriella Pasi

Bibliographic Information

  • Book Title: Soft Computing in Information Retrieval

  • Book Subtitle: Techniques and Applications

  • Editors: Fabio Crestani, Gabriella Pasi

  • Series Title: Studies in Fuzziness and Soft Computing

  • DOI: https://doi.org/10.1007/978-3-7908-1849-9

  • Publisher: Physica Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Physica-Verlag Heidelberg 2000

  • Hardcover ISBN: 978-3-7908-1299-2Published: 26 May 2000

  • Softcover ISBN: 978-3-7908-2473-5Published: 21 October 2010

  • eBook ISBN: 978-3-7908-1849-9Published: 19 March 2013

  • Series ISSN: 1434-9922

  • Series E-ISSN: 1860-0808

  • Edition Number: 1

  • Number of Pages: XII, 396

  • Topics: Information Storage and Retrieval, Artificial Intelligence, IT in Business

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