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Cross-Lingual Information Retrieval by Feature Vectors

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Natural Language Processing and Information Systems (NLDB 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4592))

Abstract

This paper investigates query translation in cross-lingual information retrieval, especially the challenges caused by ambiguity and polysemi. We base our ideas on feature vectors and our method uses context during the translation of queries. Achieving good query translation can be difficult, due to short queries lacking context information. We argue that by using information external to the query, like ontologies and document collections, the effect of ambiguity and polysemi can be reduced. Different approaches for translation of these feature vectors are proposed and discussed.

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Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology, Sem Saelandsvei 7-9, NO-7491 Trondheim, Norway

    Jeanine Lilleng & Stein L. Tomassen

Authors
  1. Jeanine Lilleng
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  2. Stein L. Tomassen
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Editor information

Zoubida Kedad Nadira Lammari Elisabeth Métais Farid Meziane Yacine Rezgui

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© 2007 Springer-Verlag Berlin Heidelberg

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Lilleng, J., Tomassen, S.L. (2007). Cross-Lingual Information Retrieval by Feature Vectors. In: Kedad, Z., Lammari, N., Métais, E., Meziane, F., Rezgui, Y. (eds) Natural Language Processing and Information Systems. NLDB 2007. Lecture Notes in Computer Science, vol 4592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73351-5_20

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  • DOI: https://doi.org/10.1007/978-3-540-73351-5_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73350-8

  • Online ISBN: 978-3-540-73351-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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