Multilingual Text Classification Using Ontologies

  • Gerard de Melo
  • Stefan Siersdorfer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4425)

Abstract

In this paper, we investigate strategies for automatically classifying documents in different languages thematically, geographically or according to other criteria. A novel linguistically motivated text representation scheme is presented that can be used with machine learning algorithms in order to learn classifications from pre-classified examples and then automatically classify documents that might be provided in entirely different languages. Our approach makes use of ontologies and lexical resources but goes beyond a simple mapping from terms to concepts by fully exploiting the external knowledge manifested in such resources and mapping to entire regions of concepts. For this, a graph traversal algorithm is used to explore related concepts that might be relevant. Extensive testing has shown that our methods lead to significant improvements compared to existing approaches.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Gerard de Melo
    • 1
  • Stefan Siersdorfer
    • 1
  1. 1.Max Planck Institute for Computer Science, SaarbrückenGermany

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