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Dynamic Threshold Selection Method for Multi-label Newspaper Topic Identification

  • Lucie Skorkovská
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8082)

Abstract

Nowadays, the multi-label classification is increasingly required in modern categorization systems. It is especially essential in the task of newspaper article topics identification. This paper presents a method based on general topic model normalisation for finding a threshold defining the boundary between the “correct” and the “incorrect” topics of a newspaper article. The proposed method is used to improve the topic identification algorithm which is a part of a complex system for acquisition and storing large volumes of text data. The topic identification module uses the Naive Bayes classifier for the multiclass and multi-label classification problem and assigns to each article the topics from a defined quite extensive topic hierarchy - it contains about 450 topics and topic categories. The results of the experiments with the improved topic identification algorithm are presented in this paper.

Keywords

topic identification multi-label text classification language modeling Naive Bayes classification 

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References

  1. 1.
    Švec, J., Hoidekr, J., Soutner, D., Vavruška, J.: Web text data mining for building large scale language modelling corpus. In: Habernal, I., Matoušek, V. (eds.) TSD 2011. LNCS, vol. 6836, pp. 356–363. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Psutka, J., Ircing, P., Psutka, J.V., Radová, V., Byrne, W., Hajič, J., Mírovský, J., Gustman, S.: Large vocabulary ASR for spontaneous Czech in the MALACH project. In: Proceedings of Eurospeech 2003, Geneva, pp. 1821–1824 (2003)Google Scholar
  3. 3.
    Skorkovská, L., Ircing, P., Pražák, A., Lehečka, J.: Automatic topic identification for large scale language modeling data filtering. In: Habernal, I., Matoušek, V. (eds.) TSD 2011. LNCS, vol. 6836, pp. 64–71. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 22–30. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Schapire, R.E., Singer, Y.: Boostexter: A boosting-based system for text categorization. In: Machine Learning, pp. 135–168 (2000)Google Scholar
  7. 7.
    Asy’arie, A.D., Pribadi, A.W.: Automatic news articles classification in indonesian language by using naive bayes classifier method. In: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services, iiWAS 2009, pp. 658–662. ACM, New York (2009)Google Scholar
  8. 8.
    McCallum, A.K.: Multi-label text classification with a mixture model trained by em. In: AAAI 1999 Workshop on Text Learning (1999)Google Scholar
  9. 9.
    Tsoumakas, G., Katakis, I.: Multi-label classification: An overview. Int. J. Data Warehousing and Mining, 1–13 (2007)Google Scholar
  10. 10.
    Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification (2004)Google Scholar
  11. 11.
    Zhang, M.L., Zhou, Z.H.: A k-nearest neighbor based algorithm for multi-label classification. In: 2005 IEEE International Conference on Granular Computing, vol. 2, pp. 718–721 (2005)Google Scholar
  12. 12.
    Yang, Y.: An evaluation of statistical approaches to text categorization. Journal of Information Retrieval 1, 67–88 (1999)CrossRefGoogle Scholar
  13. 13.
    Bracewell, D.B., Yan, J., Ren, F., Kuroiwa, S.: Category classification and topic discovery of japanese and english news articles. Electron. Notes Theor. Comput. Sci. 225, 51–65 (2009)CrossRefGoogle Scholar
  14. 14.
    Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Ircing, P., Müller, L.: Benefit of Proper Language Processing for Czech Speech Retrieval in the CL-SR Task at CLEF 2006. In: Peters, C., Clough, P., Gey, F.C., Karlgren, J., Magnini, B., Oard, D.W., de Rijke, M., Stempfhuber, M. (eds.) CLEF 2006. LNCS, vol. 4730, pp. 759–765. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Psutka, J., Švec, J., Psutka, J.V., Vaněk, J., Pražák, A., Šmídl, L., Ircing, P.: System for fast lexical and phonetic spoken term detection in a czech cultural heritage archive. EURASIP J. Audio, Speech and Music Processing (2011)Google Scholar
  17. 17.
    Skorkovská, L.: Application of lemmatization and summarization methods in topic identification module for large scale language modeling data filtering. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2012. LNCS, vol. 7499, pp. 191–198. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Kanis, J., Skorkovská, L.: Comparison of different lemmatization approaches through the means of information retrieval performance. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2010. LNCS, vol. 6231, pp. 93–100. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Sivakumaran, P., Fortuna, J., Ariyaeeinia, M.A.: Score normalisation applied to open-set, text-independent speaker identification. In: Proceedings of Eurospeech 2003, Geneva, pp. 2669–2672 (2003)Google Scholar
  20. 20.
    Zajíc, Z., Machlica, L., Padrta, A., Vaněk, J., Radová, V.: An expert system in speaker verification task. In: Proceedings of Interspeech, vol. 9, pp. 355–358. International Speech Communication Association, Brisbane (2008)Google Scholar
  21. 21.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lucie Skorkovská
    • 1
  1. 1.Faculty of Applied Sciences, Dept. of CyberneticsUniversity of West BohemiaPlzeňCzech Republic

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