UNED at WebCLEF 2008: Applying High Restrictive Summarization, Low Restrictive Information Retrieval and Multilingual Techniques

  • Enrique Amigó
  • Juan Martinez-Romo
  • Lourdes Araujo
  • Víctor Peinado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)

Abstract

This paper describes our participation in the WebCLEF 2008 task, targeted at snippet retrieval from new data. Our system assumes that the task can be tackled as a summarization problem and that the document retrieval and multilinguism treatment steps can be ignored. Our approach assumes also that the redundancy of information in the Web allows the system to be very restrictive when picking information pieces. Our evaluation results suggest that, while the first assumption is feasible, the second one is not always true.

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References

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    Jijkoun, V., de Rijke, M.: Using Centrality to Rank Web Snippets. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 737–741. Springer, Heidelberg (2008)CrossRefGoogle Scholar
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    Amigó, E., Gonzalo, J., Peinado, V., Peñas, A., Verdejo, F.: An empirical study of information synthesis tasks. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, ACL 2004 (2004)Google Scholar
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    Amigó, E., Gonzalo, J., Peinado, V., Peñas, A., Verdejo, F.: Using syntactic information to extract relevant terms for multi-document summarization. In: Proceedings of the 20th international conference on Computational Linguistics, COLING 2004 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Enrique Amigó
    • 1
  • Juan Martinez-Romo
    • 1
  • Lourdes Araujo
    • 1
  • Víctor Peinado
    • 1
  1. 1.NLP & IR Group at UNED, ETSI Informática UNEDMadridSpain

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