Multilingual Text Classification Using Ontologies

  • Gerard de Melo
  • Stefan Siersdorfer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4425)


In this paper, we investigate strategies for automatically classifying documents in different languages thematically, geographically or according to other criteria. A novel linguistically motivated text representation scheme is presented that can be used with machine learning algorithms in order to learn classifications from pre-classified examples and then automatically classify documents that might be provided in entirely different languages. Our approach makes use of ontologies and lexical resources but goes beyond a simple mapping from terms to concepts by fully exploiting the external knowledge manifested in such resources and mapping to entire regions of concepts. For this, a graph traversal algorithm is used to explore related concepts that might be relevant. Extensive testing has shown that our methods lead to significant improvements compared to existing approaches.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bel, N., Koster, C.H.A., Villegas, M.: Cross-lingual text categorization. In: Koch, T., Sølvberg, I.T. (eds.) ECDL 2003. LNCS, vol. 2769, pp. 126–139. Springer, Heidelberg (2003)Google Scholar
  2. 2.
    García Adeva, J.J., Calvo, R.A., de Ipiña, D.L.: Multilingual approaches to text categorisation. Europ. J. for the Informatics Professional VI(3), 43–51 (2005)Google Scholar
  3. 3.
    Jalam, R.: Apprentissage automatique et catégorisation de textes multilingues. PhD thesis, Université Lumière Lyon 2, Lyon, France (2003)Google Scholar
  4. 4.
    Olsson, J.S., et al.: Cross-language text classification. In: Proc. SIGIR 2005, pp. 645–646 (2005), doi:10.1145/1076034.1076170Google Scholar
  5. 5.
    Rigutini, L., et al.: An EM based training algorithm for cross-language text categorization. In: Proc. Web Intelligence 2005, Washington, DC, USA, pp. 529–535 (2005)Google Scholar
  6. 6.
    Oard, D.W., Dorr, B.J.: A survey of multilingual text retrieval. Technical report, University of Maryland at College Park, College Park, MD, USA (1996)Google Scholar
  7. 7.
    de Buenaga Rodríguez, M., et al.: Using WordNet to complement training information in text categorization. In: Proc. 2nd RANLP (1997)Google Scholar
  8. 8.
    Moschitti, A., Basili, R.: Complex linguistic features for text classification: a comprehensive study. In: McDonald, S., Tait, J. (eds.) ECIR 2004. LNCS, vol. 2997, Springer, Heidelberg (2004)Google Scholar
  9. 9.
    Ifrim, G., Theobald, M., Weikum, G.: Learning word-to-concept mappings for automatic text classification. In: Proc. 22nd ICML - LWS, pp. 18–26 (2005)Google Scholar
  10. 10.
    Verdejo, F., Gonzalo, J., Peñas, A., et al.: Evaluating wordnets in cross-language text retrieval. In: Proceedings LREC (2000)Google Scholar
  11. 11.
    Scott, S., Matwin, S.: Text classification using WordNet hypernyms. In: Proc. Worksh. Usage of WordNet in NLP Systems at COLING-98, pp. 38–44. Sage, Thousand Oaks (1998)Google Scholar
  12. 12.
    Bloehdorn, S., Hotho, A.: Boosting for text classification with semantic features. In: Proc. Worksh. on Mining for/from the Semantic Web at KDD 2004, pp. 70–87 (2004)Google Scholar
  13. 13.
    Ramakrishnanan, G., et al.: Text representation with WordNet synsets using soft sense disambiguation. Ing. systèmes d’information 8(3), 55–70 (2003)CrossRefGoogle Scholar
  14. 14.
    Gliozzo, A.M., et al.: Cross language text categ. by acq. multil. domain models from comp. corpora. In: Proc. ACL Worksh. Building and Using Parallel Texts (2005),
  15. 15.
    Dumais, S.T., et al.: Automatic cross-language retrieval using latent semantic indexing. In: AAAI Symposium on CrossLanguage Text and Speech Retrieval (1997),
  16. 16.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)MATHGoogle Scholar
  17. 17.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002), CrossRefGoogle Scholar
  18. 18.
    AltaVista: Babel fish translation (2006),
  19. 19.
    Fellbaum, C.: WordNet: An Electronic Lexical Database (Language, Speech, and Communication). MIT Press, Cambridge (1998)Google Scholar
  20. 20.
    Farreres, X., Rigau, G., Rodríguez, H.: Using WordNet for building WordNets. In: Proc. Conf. Use of WordNet in NLP Systems, pp. 65–72 (1998)Google Scholar
  21. 21.
    Theobald, M., Schenkel, R., Weikum, G.: Exploiting structure, annotation, and ontological knowledge for automatic classification of XML data. In: 6th Intl. Worksh. Web and Databases, pp. 1–6 (2003)Google Scholar
  22. 22.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)MATHGoogle Scholar
  23. 23.
    Joachims, T.: Making large-scale support vector machine learning practical. Advances in Kernel Methods: Support Vector Machines (1999),
  24. 24.
    Daudé, J., et al.: Making Wordnet mappings robust. In: Proc. Congreso de la Sociedad Española para el Procesamiento del Lenguage Natural (SEPLN) (2003)Google Scholar
  25. 25.
    Wikimedia Foundation: Wikipedia (2006),

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Gerard de Melo
    • 1
  • Stefan Siersdorfer
    • 1
  1. 1.Max Planck Institute for Computer Science, SaarbrückenGermany

Personalised recommendations