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Neural Processing Letters

, Volume 22, Issue 2, pp 149–161 | Cite as

Merging Strategy for Cross-Lingual Information Retrieval Systems based on Learning Vector Quantization

  • M. T. Martín-Valdivia
  • F. Martínez-Santiago
  • L. A. Ureña-López
Article

Abstract

We present a new approach based on neural networks to solve the merging strategy problem for Cross-Lingual Information Retrieval (CLIR). In addition to language barrier issues in CLIR systems, how to merge a ranked list that contains documents in different languages from several text collections is also critical. We propose a merging strategy based on competitive learning to obtain a single ranking of documents merging the individual lists from the separate retrieved documents. The main contribution of the paper is to show the effectiveness of the Learning Vector Quantization (LVQ) algorithm in solving the merging problem. In order to investigate the effects of varying the number of codebook vectors, we have carried out several experiments with different values for this parameter. The results demonstrate that the LVQ algorithm is a good alternative merging strategy.

Keywords

cross-lingual information retrieval Kohonen neural network learning vector quantization merging strategy retrieve status value 

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Copyright information

© Springer 2005

Authors and Affiliations

  • M. T. Martín-Valdivia
    • 1
  • F. Martínez-Santiago
    • 1
  • L. A. Ureña-López
    • 1
  1. 1.Departamento de InformáticaUniversity of JaénJaénSpain

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