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One of the important factors that affects the performance of Cross LanguageInformation Retrieval(CLIR)is the quality of translations being employed inCLIR. In order to improve the quality of translations, it is important toexploit available resources efficiently. Employing different translationresources with different characteristics has many challenges. In this paper, wepropose a method for exploiting available translation resources simultaneously.This method employs Learning to Rank(LTR) for exploiting different translationresources. To apply LTR methods for query translation, we define differenttranslation relation based features in addition to context based features. Weuse the contextual information contained in translation resources forextracting context based features.The proposed method uses LTR to construct atranslation ranking model based on defined features. The constructed model isused for ranking translation candidates of query words. To evaluate theproposed method we do English-Persian CLIR, in which we employ the translationranking model to find translations of English queries and employ thetranslations to retrieve Persian documents. Experimental results show that ourapproach significantly outperforms single resource based CLIR methods.

Learning to Exploit Different Translation Resources for Cross Language Information Retrieval