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Using IR Techniques for Text Classification in Document Analysis

  • Conference paper
SIGIR ’94

Abstract

This paper presents the INFOCLAS system applying statistical methods of information retrieval for the classification of German business letters into corresponding message types such as order, offer, enclosure, etc. INFOCLAS is a first step towards the understanding of documents proceeding to a classification-driven extraction of information. The system is composed of two main modules: the central indexer (extraction and weighting of indexing terms) and the classifier (classification of business letters into given types). The system employs several knowledge sources including a letter database, word frequency statistics for German, lists of message type specific words, morphological knowledge as well as the underlying document structure. As output, the system evaluates a set of weighted hypotheses about the type of the actual letter. Classification of documents allow the automatic distribution or archiving of letters and is also an excellent starting point for higher-level document analysis.1

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Author information

Authors and Affiliations

  1. German Research Center for Artificial Intelligence (DFKI), P.O. Box 20 80, D-67608, Kaiserslautern, Germany

    Rainer Hoch

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  1. Rainer Hoch
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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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Hoch, R. (1994). Using IR Techniques for Text Classification in Document Analysis. In: Croft, B.W., van Rijsbergen, C.J. (eds) SIGIR ’94. Springer, London. https://doi.org/10.1007/978-1-4471-2099-5_4

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  • DOI: https://doi.org/10.1007/978-1-4471-2099-5_4

  • 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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