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)

Abstract

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yang, Y., Liu, X.: A Re-examination of Text Categorization Methods. In: Proceedings of SIGIR-99, 22nd ACM International Conference on Research and Development in Information Retrieval, Berkeley (1996)Google Scholar
  2. 2.
    Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Forman, G.: An Extensive Empirical Study of Feature Selection Metrics for Text Classification. Journal of Machine Learning Research 3, 1289–1305 (2003)CrossRefMATHGoogle Scholar
  4. 4.
    Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)CrossRefGoogle Scholar
  5. 5.
    Özgür, A.: Supervised and Unsupervised Machine Learning Techniques for Text Document Categorization. MS Thesis, Boğaziçi University, Istanbul (2004)Google Scholar
  6. 6.
    Joachims, T.: Making Large-Scale SVM Learning Practical. In: Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge (1999)Google Scholar
  7. 7.
    Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the 14th International Conference on Machine Learning, pp. 412–420 (1997)Google Scholar
  8. 8.
    Mladenic, D., Grobelnic, M.: Feature Selection for Unbalanced Class Distribution and Naive Bayes. In: Proceedings of the 16th International Conference on Machine Learning, pp. 258–267 (1999)Google Scholar
  9. 9.
    Debole, F., Sebastiani, F.: Supervised Term Weighting for Automated Text Categorization. In: Proceedings of SAC-03, 18th ACM Symposium on Applied Computing, pp. 784–788. ACM Press, New York (2003)CrossRefGoogle Scholar
  10. 10.
    Aizawa, A.: Linguistic Techniques to Improve the Performance of Automatic Text Categorization. In: Proceedings of 6th Natural Language Processing Pacific Rim Symposium, Tokyo, pp. 307–314 (2001)Google Scholar
  11. 11.
    Özgür, A., Özgür, L., Güngör, T.: Text Categorization with Class-Based and Corpus-Based Keyword Selection. In: Yolum, P., et al. (eds.) ISCIS 2005. LNCS, vol. 3733, pp. 607–616. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Porter, M.F.: An Algorithm for Suffix Stripping. Program 14, 130–137 (1980)Google Scholar
  14. 14.
    Salton, G., Buckley, C.: Term Weighting Approaches in Automatic Text Retrieval. Information Processing and Management 24(5), 513–523 (1988)CrossRefGoogle Scholar
  15. 15.
    Karypis, G.: Cluto 2.0 Clustering Toolkit (2004), http://www.users.cs.umn.edu/~karypis/cluto
  16. 16.
    TREC. Text Retrieval Conference (1999), http://trec.nist.gov
  17. 17.
    Lewis, D.D.: Reuters-21578 Document Corpus V1.0, http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html
  18. 18.
    Han, E.-H.S., et al.: WebAce: A Web Agent for Document Categorization and Exploration. In: Proceedings of the 2nd International Conference on Autonomous Agents (1998)Google Scholar
  19. 19.
    Özgür, L., Güngör, T., Gürgen, F.: Adaptive Anti-Spam Filtering for Agglutinative Languages. A Special Case for Turkish. Pattern Recognition Letters 25(16), 1819–1831 (2004)CrossRefGoogle Scholar
  20. 20.
    Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(5), 1–47 (2002)CrossRefMathSciNetGoogle Scholar

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

Personalised recommendations