English-to-French CLIR: A Knowledge-Light Approach through Character N-Grams Alignment

  • Jesús Vilares
  • Michael P. Oakes
  • Manuel Vilares
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5152)


This paper describes an extension of our work presented in the robust English-to-French bilingual task of the CLEF 2007 workshop, a knowledge-light approach for query translation in Cross-Language Information Retrieval systems. Our work is based on the direct translation of character n-grams, avoiding the need for word normalization during indexing or translation, and also dealing with out-of-vocabulary words. Moreover, since such a solution does not rely on language-specific processing, it can be used with languages of very different nature even when linguistic information and resources are scarce or unavailable. The results obtained have been very positive, and support the findings from our previous English-to-Spanish experiments.


Mutual Information Mean Average Precision Association Measure Word Alignment Translation Probability 
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  1. 1. (visited on November 2007)
  2. 2.
    McNamee, P., Mayfield, J.: JHU/APL experiments in tokenization and non-word translation. In: Peters, C., Gonzalo, J., Braschler, M., Kluck, M. (eds.) CLEF 2003. LNCS, vol. 3237, pp. 85–97. Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Vilares, J., Oakes, M.P., Tait, J.I.: A first approach to CLIR using character n-grams alignment. 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. 111–118. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Computational Linguistics 29(1), 19–51 (2003), (visited on November 2007)CrossRefGoogle Scholar
  5. 5.
    Vilares, J., Oakes, M.P., Vilares, M.: A knowledge-light approach to query translation in cross-language information retrieval. In: Proc. of International Conference on Recent Advances in Natural Language Processing (RANLP 2007), pp. 624–630 (2007)Google Scholar
  6. 6.
    Vilares, J., Oakes, M.P., Vilares, M.: Character n-grams translation in cross-language information retrieval. In: Kedad, Z., Lammari, N., Métais, E., Meziane, F., Rezgui, Y. (eds.) NLDB 2007. LNCS, vol. 4592, pp. 217–228. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Koehn, P., Och, F.J., Marcu, D.: Statistical phrase-based translation. In: NAACL 2003: Proc. of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 48–54 (2003)Google Scholar
  8. 8.
    Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  9. 9.
    Di Nunzio, G.M., Ferro, N., Mandl, T., Peters, C.: CLEF 2007 ad hoc track overview. In: Peters, C., et al. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 13–32. Springer, Heidelberg (2008)Google Scholar
  10. 10. (visited on November 2007)
  11. 11.
    Amati, G., van Rijsbergen, C.J.: Probabilistic models of information retrieval based on measuring divergence from randomness. ACM Transactions on Information Systems 20(4), 357–389 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jesús Vilares
    • 1
  • Michael P. Oakes
    • 2
  • Manuel Vilares
    • 3
  1. 1.Dept. of Computer ScienceUniversity of A CoruñaA Coruña(Spain)
  2. 2.School of Computing and TechnologyUniversity of SunderlandSunderland(United Kingdom)
  3. 3.Dept. of Computer ScienceUniversity of VigoOurense(Spain)

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