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Text Classification of Technical Papers Based on Text Segmentation

  • Thien Hai Nguyen
  • Kiyoaki Shirai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7934)

Abstract

The goal of this research is to design a multi-label classification model which determines the research topics of a given technical paper. Based on the idea that papers are well organized and some parts of papers are more important than others for text classification, segments such as title, abstract, introduction and conclusion are intensively used in text representation. In addition, new features called Title Bi-Gram and Title SigNoun are used to improve the performance. The results of the experiments indicate that feature selection based on text segmentation and these two features are effective. Furthermore, we proposed a new model for text classification based on the structure of papers, called Back-off model, which achieves 60.45% Exact Match Ratio and 68.75% F-measure. It was also shown that Back-off model outperformed two existing methods, ML-kNN and Binary Approach.

Keywords

Text Classification Multi-label Classification Text Segmentation Supervised Learning 

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References

  1. 1.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRefGoogle Scholar
  2. 2.
    Rahmoun, A., Elberrichi, Z.: Experimenting n-grams in text categorization. Int. Arab J. Inf. Technol., 377–385 (2007)Google Scholar
  3. 3.
    Cao, M.D., Gao, X.: Combining contents and citations for scientific document classification. In: Australian Conference on Artificial Intelligence, pp. 143–152 (2005)Google Scholar
  4. 4.
    Zhang, M., Gao, X., Cao, M.D., Ma, Y.: Modelling citation networks for improving scientific paper classification performance. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 413–422. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Nomoto, T., Matsumoto, Y.: Exploiting text structure for topic identification. In: Proceedings of the 4th Workshop on Very Large Corpora, pp. 101–112 (1996)Google Scholar
  6. 6.
    Larkey, L.S.: A patent search and classification system. In: Proceedings of the Fourth ACM Conference on Digital Libraries, DL 1999, pp. 179–187. ACM, New York (1999)CrossRefGoogle Scholar
  7. 7.
    Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer US (2010)Google Scholar
  8. 8.
    Zhang, M.L., Zhou, Z.H.: Ml-knn: A lazy learning approach to multi-label learning. Pattern Recognition 40(7), 2038–2048 (2007)zbMATHCrossRefGoogle Scholar
  9. 9.
    Tsoumakas, G., Spyromitros-Xioufis, E., Vilcek, J., Vlahavas, I.: Mulan: A java library for multi-label learning. Journal of Machine Learning Research 12, 2411–2414 (2011)MathSciNetGoogle Scholar
  10. 10.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  11. 11.
    Morgan, W.: Statistical hypothesis tests for NLP, http://cs.stanford.edu/people/wmorgan/sigtest.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Thien Hai Nguyen
    • 1
  • Kiyoaki Shirai
    • 1
  1. 1.Japan Advanced Institute of Science and TechnologyJapan

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