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Cross-Language Information Retrieval with Latent Topic Models Trained on a Comparable Corpus

  • Ivan Vulić
  • Wim De Smet
  • Marie-Francine Moens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7097)

Abstract

In this paper we study cross-language information retrieval using a bilingual topic model trained on comparable corpora such as Wikipedia articles. The bilingual Latent Dirichlet Allocation model (BiLDA) creates an interlingual representation, which can be used as a translation resource in many different multilingual settings as comparable corpora are available for many language pairs. The probabilistic interlingual representation is incorporated in a statistical language model for information retrieval. Experiments performed on the English and Dutch test datasets of the CLEF 2001-2003 CLIR campaigns show the competitive performance of our approach compared to cross-language retrieval methods that rely on pre-existing translation dictionaries that are hand-built or constructed based on parallel corpora.

Keywords

Cross-language retrieval topic models comparable corpora document models multilingual retrieval Wikipedia 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ivan Vulić
    • 1
  • Wim De Smet
    • 1
  • Marie-Francine Moens
    • 1
  1. 1.Department of Computer ScienceK.U. LeuvenBelgium

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