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)


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.


WordNet Text Categorization Semantics 


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  1. 1.
    Gliozzo, A.M., Strapparava, C., Dagan, I.: Investigating Unsupervised Learning for Text Categorization Bootstrapping. In: Proc. of EMNLP (2005)Google Scholar
  2. 2.
    Liu, B., Li, X., Lee, W.S., Yu, P.S.: Text Classification by Labeling Words. In: Proc. 19th Nat’l Conf. Artificial Intelligence (2004)Google Scholar
  3. 3.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proc. of the Workshop on Computational Learning Theory (1998)Google Scholar
  4. 4.
    de Buenaga Rodriguez, M., Gomez-Hidalgo, J., Diaz- Agudo, B.: Using WordNet to complement training information in text categorization. In: Proc. of RANLP (1997)Google Scholar
  5. 5.
    Hotho, A., Staab, S., Stumme, G.: Wordnet Improves Text Document Clustering. In: Proc. of the Semantic Web Workshop at SIGIR (2003)Google Scholar
  6. 6.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  7. 7.
    Ide, N., Véronis, J.: Word sense disambiguation: The state of the art. Computational Linguistics 24(1), 1–40 (1998)Google Scholar
  8. 8.
    Joachims, T.: Transductive inference for text classification using support vector machines. In: Proc. 16th International Conf. on Machine Learning, pp. 200–209 (1999)Google Scholar
  9. 9.
    Kehagias, A., Petridis, V., Kaburlasos, V., Fragkou, P.: A comparison of word- and sense-based text classification using several classification algorithms. Journal of Intelligent Information Systems 21(3), 227–247 (2003)CrossRefGoogle Scholar
  10. 10.
    Moldovan, D.I., Mihalcea, R.: Using WordNet and Lexical Operators to Improve Internet Searches. IEEE lnternet Computing 4(1), 34–43 (2000)CrossRefGoogle Scholar
  11. 11.
    Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine Learning, 103–134 (2000)Google Scholar
  12. 12.
    Scott, S., Matwin, S.: Text classification using WordNet hypernyms. In: Proc. Coling-ACL 1998, pp. 45–52 (1998)Google Scholar
  13. 13.
    Peng, X., Choi, B.: Document classifications based on word semantic hierarchies. In: Proc. of the International Conf. on Artificial Intelligence and Application (AIA 2005), pp. 362–367 (2005)Google Scholar
  14. 14.
    Banerjee, S., Pedersen, T.: An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, pp. 136–145. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)CrossRefGoogle Scholar
  16. 16.
    Mansuy, T.N., Hilderman, R.J.: A Characterization of Wordnet Features in Boolean Models For Text Classification. In: AusDM 2006, pp. 103–109 (2006)Google Scholar
  17. 17.
    Vapnik, V.: The nature of statistical learning theory. Springer, Heidelberg (1995)CrossRefzbMATHGoogle Scholar
  18. 18.
    Chen, W., Zhu, J., Wu, H., Yao, T.: Automatic learning features using bootstrapping for text categorization. In: Gelbukh, A. (ed.) CICLing 2004. LNCS, vol. 2945, pp. 571–579. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Zhu, X.-J.: Semi-Supervised Learning Literature Survey (2007),
  20. 20.
    Ko, Y., Seo, J.: Automatic text categorization by unsupervised learning. In: Proc. of COLING 2000 (2000)Google Scholar
  21. 21.
    Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proc. of SIGIR 1999 (1999)Google Scholar

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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