Classification of Skewed and Homogenous Document Corpora with Class-Based and Corpus-Based Keywords

  • Arzucan Özgür
  • Tunga Güngör
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4314)


In this paper, we examine the performance of the two policies for keyword selection over standard document corpora of varying properties. While in corpus-based policy a single set of keywords is selected for all classes globally, in class-based policy a distinct set of keywords is selected for each class locally. We use SVM as the learning method and perform experiments with boolean and tf-idf weighting. In contrast to the common belief, we show that using keywords instead of all words generally yields better performance and tf-idf weighting does not always outperform boolean weighting. Our results reveal that corpus-based approach performs better for large number of keywords while class-based approach performs better for small number of keywords. In skewed datasets, class-based keyword selection performs consistently better than corpus-based approach in terms of macro-averaged F-measure. In homogenous datasets, performances of class-based and corpus-based approaches are similar except for small number of keywords.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Arzucan Özgür
    • 1
  • Tunga Güngör
    • 1
  1. 1.Boğaziçi University, Computer Engineering Department, Bebek, 34342 İstanbulTurkey

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