A learning to rank approach for cross-language information retrieval exploiting multiple translation resources. (5th March 2019)

• Record Type: Journal Article
• Authors: Azarbonyad, Hosein; Shakery, Azadeh; Faili, Heshaam
• Is Part Of: Natural language engineering. Volume 25:Part 3(2019)
• Page Start: 363
• Page End: 384
• Publication Date: 2019-03-05
• Subjects: cross-language information retrieval, -- learning to rank, -- translation resources, -- resource combination for CLIR; Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S1351324919000032
• Languages: English
• ISSNs: 1351-3249

Abstract

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.