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OOIS 2000 pp 349–357Cite as

A Probabilistic Model for Classification of Multiple-Record Web Documents

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Abstract

The amount of information available on the World Wide Web, which appear in various Web documents, have been increasing dramatically in recent years. Classification of Web documents is becoming a more significant method for organizing such information. In this paper, we adopt a probabilistic model to classify Web documents into relevant documents and irrelevant documents with respect to an application ontology. Our model is based on the multivariant statistical analysis and is different from the conventional probabilistic information retrieval models. The experiments we have conducted using our probabilistic model look promising in terms of classification of multiple-record Web documents.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Brigham Young University, Provo, Utah, 84602, USA

    June Tang & Yiu-Kai Ng

Authors
  1. June Tang
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  2. Yiu-Kai Ng
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Editor information

Editors and Affiliations

  1. School of Computing, Information Systems and Mathematics, South Bank University, 103 Borough Road, London, UK

    Dilip Patel  & Shushma Patel  & 

  2. School of Computing, London Guildhall University, 100 Minories, London, UK

    Islam Choudhury

  3. Department of Information Systems and Computing, Brunel University, Uxbridge, London, UK

    Sergio de Cesare

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© 2001 Springer-Verlag London Limited

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Cite this paper

Tang, J., Ng, YK. (2001). A Probabilistic Model for Classification of Multiple-Record Web Documents. In: Patel, D., Choudhury, I., Patel, S., de Cesare, S. (eds) OOIS 2000. Springer, London. https://doi.org/10.1007/978-1-4471-0299-1_29

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  • DOI: https://doi.org/10.1007/978-1-4471-0299-1_29

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-420-8

  • Online ISBN: 978-1-4471-0299-1

  • eBook Packages: Springer Book Archive

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