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Fully Automatic Text Categorization by Exploiting WordNet

  • Jianqiang Li
  • Yu Zhao
  • Bo Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5839)

Abstract

This paper proposes a Fully Automatic Categorization approach for Text (FACT) by exploiting the semantic features from WordNet and document clustering. In FACT, the training data is constructed automatically by using the knowledge of the category name. With the support of WordNet, it first uses the category name to generate a set of features for the corresponding category. Then, a set of documents is labeled according to such features. To reduce the possible bias originating from the category name and generated features, document clustering is used to refine the quality of initial labeling. The training data are subsequently constructed to train the discriminative classifier. The empirical experiments show that the best performance of FACT can achieve more than 90% of the baseline SVM classifiers in F1 measure, which demonstrates the effectiveness of the proposed approach.

Keywords

WordNet Text Categorization Semantics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jianqiang Li
    • 1
  • Yu Zhao
    • 1
  • Bo Liu
    • 1
  1. 1.NEC Laboratories ChinaBeijingChina

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