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International Journal of Information and Communication Technology Research (IJICT، جلد ۶، شماره ۱، صفحات ۵۵-۶۸
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عنوان انگلیسی Learning to Exploit Different Translation Resources for Cross Language Information Retrieval
چکیده انگلیسی مقاله 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 CLR methods.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله | Hosein Azarbonyad


| Azadeh Shakery


| Heshaam Faili


نشانی اینترنتی http://ijict.itrc.ac.ir/browse.php?a_code=A-10-27-111&slc_lang=fa&sid=1
فایل مقاله اشکال در دسترسی به فایل - ./files/site1/rds_journals/417/article-417-1212414.pdf
کد مقاله (doi)
زبان مقاله منتشر شده fa
موضوعات مقاله منتشر شده فناوری اطلاعات
نوع مقاله منتشر شده پژوهشی
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