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Reducing the Plagiarism Detection Search Space on the Basis of the Kullback-Leibler Distance

  • Alberto Barrón-Cedeño
  • Paolo Rosso
  • José-Miguel Benedí
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5449)

Abstract

Automatic plagiarism detection considering a reference corpus compares a suspicious text to a set of original documents in order to relate the plagiarised fragments to their potential source. Publications on this task often assume that the search space (the set of reference documents) is a narrow set where any search strategy will produce a good output in a short time. However, this is not always true. Reference corpora are often composed of a big set of original documents where a simple exhaustive search strategy becomes practically impossible.

Before carrying out an exhaustive search, it is necessary to reduce the search space, represented by the documents in the reference corpus, as much as possible. Our experiments with the METER corpus show that a previous search space reduction stage, based on the Kullback-Leibler symmetric distance, reduces the search process time dramatically. Additionally, it improves the Precision and Recall obtained by a search strategy based on the exhaustive comparison of word n-grams.

Keywords

Search Space Exhaustive Search Feature Selection Technique Reference Document Search Space Reduction 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alberto Barrón-Cedeño
    • 1
  • Paolo Rosso
    • 1
  • José-Miguel Benedí
    • 1
  1. 1.Department of Information Systems and ComputationUniversidad Politécnica de ValenciaValenciaSpain

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