Application of Lemmatization and Summarization Methods in Topic Identification Module for Large Scale Language Modeling Data Filtering

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7499)


The paper presents experiments with the topic identification module which is a part of a complex system for acquisition and storing large volumes of text data. The topic identification module processes each acquired data item and assigns it topics from a defined topic hierarchy. The topic hierarchy is quite extensive – it contains about 450 topics and topic categories. It can easily happen that for some narrowly focused topic there is not enough data for the topic identification training. Lemmatization is shown to improve the results when dealing with sparse data in the area of information retrieval, therefore the effects of lemmatization on topic identification results is studied in the paper. On the other hand, since the system is used for processing large amounts of data, a summarization method was implemented and the effect of using only the summary of an article on the topic identification accuracy is studied.


topic identification lemmatization summarization language modeling 


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  1. 1.
    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
  2. 2.
    Edmundson, H.P.: New methods in automatic extracting. J. ACM 16(2), 264–285 (1969)zbMATHCrossRefGoogle Scholar
  3. 3.
    Erkan, G., Radev, D.R.: Lexrank: graph-based lexical centrality as salience in text summarization. J. Artif. Int. Res. 22(1), 457–479 (2004)Google Scholar
  4. 4.
    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
  5. 5.
    Kanis, J., Müller, L.: Automatic Lemmatizer Construction with Focus on OOV Words Lemmatization. In: Matoušek, V., Mautner, P., Pavelka, T. (eds.) TSD 2005. LNCS (LNAI), vol. 3658, pp. 132–139. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Kupiec, J., Pedersen, J., Chen, F.: A trainable document summarizer. In: Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1995, pp. 68–73. ACM, New York (1995)CrossRefGoogle Scholar
  8. 8.
    Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159–165 (1958)MathSciNetCrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    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 (2011)Google Scholar
  11. 11.
    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
  12. 12.
    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

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

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

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