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

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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.

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Authors and Affiliations

  1. Departamento de Informática, University of Jaén, Campus Las Lagunillas s/n, Edif A3, Jaén, E-23071, Spain

    M. T. Martín-Valdivia, F. Martínez-Santiago & L. A. Ureña-López

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  1. M. T. Martín-Valdivia
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  2. F. Martínez-Santiago
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  3. L. A. Ureña-López
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Correspondence to M. T. Martín-Valdivia.

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Martín-Valdivia, M.T., Martínez-Santiago, F. & Ureña-López, L.A. Merging Strategy for Cross-Lingual Information Retrieval Systems based on Learning Vector Quantization. Neural Process Lett 22, 149–161 (2005). https://doi.org/10.1007/s11063-005-2659-y

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