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A learning to rank approach for cross-language information retrieval exploiting multiple translation resources

Published online by Cambridge University Press:  05 March 2019

Hosein Azarbonyad
Affiliation:
Science Faculty, Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
Azadeh Shakery*
Affiliation:
Department of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
Heshaam Faili
Affiliation:
Department of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
*
*Corresponding author. Email: shakery@ut.ac.ir

Abstract

Cross-language information retrieval (CLIR), finding information in one language in response to queries expressed in another language, has attracted much attention due to the explosive growth of multilingual information in the World Wide Web. One important issue in CLIR is how to apply monolingual information retrieval (IR) methods in cross-lingual environments. Recently, learning to rank (LTR) approach has been successfully employed in different IR tasks. In this paper, we use LTR for CLIR. In order to adapt monolingual LTR techniques in CLIR and pass the barrier of language difference, we map monolingual IR features to CLIR ones using translation information extracted from different translation resources. The performance of CLIR is highly dependent on the size and quality of available bilingual resources. Effective use of available resources is especially important in low-resource language pairs. In this paper, we further propose an LTR-based method for combining translation resources in CLIR. We have studied the effectiveness of the proposed approach using different translation resources. Our results also show that LTR can be used to successfully combine different translation resources to improve the CLIR performance. In the best scenario, the LTR-based combination method improves the performance of single-resource-based CLIR method by 6% in terms of Mean Average Precision.

Type
Article
Copyright
© Cambridge University Press 2019 

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