  One of the important factors that affects the performance of Cross Language
Information Retrieval(CLIR)is the quality of translations being employed in
CLIR. In order to improve the quality of translations, it is important to
exploit available resources efficiently. Employing different translation
resources with different characteristics has many challenges. In this paper, we
propose a method for exploiting available translation resources simultaneously.
This method employs Learning to Rank(LTR) for exploiting different translation
resources. To apply LTR methods for query translation, we define different
translation relation based features in addition to context based features. We
use the contextual information contained in translation resources for
extracting context based features.The proposed method uses LTR to construct a
translation ranking model based on defined features. The constructed model is
used for ranking translation candidates of query words. To evaluate the
proposed method we do English-Persian CLIR, in which we employ the translation
ranking model to find translations of English queries and employ the
translations to retrieve Persian documents. Experimental results show that our
approach significantly outperforms single resource based CLIR methods.
