Using the X-IOTA System in Mono- and Bilingual Experiments at CLEF 2005

  • Loïc Maisonnasse
  • Gilles Sérasset
  • Jean-Pierre Chevallet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4022)


This document describes the CLIPS experiments in the CLEF 2005 campaign. We used a surface-syntactic parser in order to extract new indexing terms. These terms are considered syntactic dependencies. Our goal was to evaluate their relevance for an information retrieval task. We used them in different forms in different information retrieval models, in particular in a language model. For the bilingual task, we tried two simple tests of Spanish and German to French retrieval; for the translation we used a lemmatizer and a dictionary.


Information Retrieval Language Model Average Precision Term Frequency Dependency Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chevallet, J.P., Sérasset, G.: Using Surface-Syntactic Parser and Deviation from Randomness. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 38–49. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Koster, C.H.A.: Head/Modifier Frames for Information Retrieval. In: Gelbukh, A. (ed.) CICLing 2004. LNCS, vol. 2945, pp. 420–432. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Strzalkowski, T., Stein, G.C., Wise, G.B., Carballo, J.P., Tapanainen, P., Jarvinen, T., Voutilainen, A., Karlgren, J.: Natural language information retrieval: TREC-7 report. In: Text REtrieval Conference, pp. 164–173 (1998)Google Scholar
  4. 4.
    Matsumura, A., Takasu, A., Adachi, J.: The effect of information retrieval method using dependency relationship between words. In: Proceedings of the RIAO 2000 Conference, pp. 1043–1058 (2000)Google Scholar
  5. 5.
    Metzler, D.P., Haas, S.W.: The constituent object parser: syntactic structure matching for information retrieval. ACM Trans. Inf. Syst. 7(3), 292–316 (1989)CrossRefGoogle Scholar
  6. 6.
    Smeaton, A.F.: Using NLP or NLP resources for information retrieval tasks. In: Strzalkowski, T. (ed.) Natural language information retrieval, pp. 99–111. Kluwer Academic Publishers, Dordrecht (1999)Google Scholar
  7. 7.
    Ait-Mokhtar, S., Chanod, J.P., Roux, C.: Robustness beyond shallowness: incremental deep parsing. Nat. Lang. Eng. 8(3), 121–144 (2002)CrossRefGoogle Scholar
  8. 8.
    Gao, J., Nie, J.Y., Wu, G., Cao, G.: Dependence language model for information retrieval. In: SIGIR 2004: Proceedings of the 27th annual international ACM SIGIR, pp. 170–177. ACM Press, New York (2004)Google Scholar
  9. 9.
    Nallapati, R., Allan, J.: Capturing term dependencies using a language model based on sentence trees. In: CIKM 2002: Proceedings of the eleventh international conference on Information and knowledge management, pp. 383–390. ACM Press, New York (2002)CrossRefGoogle Scholar
  10. 10.
    Gelbukh, A.F., Sidorov, G.: Approach to construction of automatic morphological analysis systems for inflective languages with little effort. In: Gelbukh, A.F. (ed.) CICLing 2003. LNCS, vol. 2588, pp. 215–220. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Loïc Maisonnasse
    • 1
  • Gilles Sérasset
    • 1
  • Jean-Pierre Chevallet
    • 2
  1. 1.Laboratoire CLIPS-IMAGGrenobleFrance
  2. 2.IPAL-CNRS, I2R A*STARNational University of Singapore 

Personalised recommendations