Building a multi-domain comparable corpus using a learning to rank method. (15th June 2016)

• Record Type: Journal Article
• Authors: RAHIMI, RAZIEH; SHAKERY, AZADEH; DADASHKARIMI, JAVID; ARIANNEZHAD, MOZHDEH; DEHGHANI, MOSTAFA; ESFAHANI, HOSSEIN NASR
• Editors: Rapp, Reinhard; Sharoff, Serge; Zweigenbaum, Pierre
• Is Part Of: Natural language engineering. Volume 22:Part 4(2016)
• Page Start: 627
• Page End: 653
• Publication Date: 2016-06-15
• Subjects: Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S1351324916000164
• Languages: English
• ISSNs: 1351-3249

Abstract

Abstract: Comparable corpora are key translation resources for both languages and domains with limited linguistic resources. The existing approaches for building comparable corpora are mostly based on ranking candidate documents in the target language for each source document using a cross-lingual retrieval model. These approaches also exploit other evidence of document similarity, such as proper names and publication dates, to build more reliable alignments. However, the importance of each evidence in the scores of candidate target documents is determined heuristically. In this paper, we employ a learning to rank method for ranking candidate target documents with respect to each source document. The ranking model is constructed by defining each evidence for similarity of bilingual documents as a feature whose weight is learned automatically. Learning feature weights can significantly improve the quality of alignments, because the reliability of features depends on the characteristics of both source and target languages of a comparable corpus. We also propose a method to generate appropriate training data for the task of building comparable corpora. We employed the proposed learning-based approach to build a multi-domain English–Persian comparable corpus which covers twelve different domains obtained from Open Directory Project. Experimental results show that the created alignments have high degrees of comparability. Comparison with existing approaches for building comparable corpora shows that our learning-based approach improves both quality and coverage of alignments.