A Probabilistic Translation Method for Dictionary-based Cross-lingual Information Retrieval in Agglutinative Languages

Abstract. Translation ambiguity, out of vocabulary words and missing some translations in bilingual dictionaries make dictionary-based Cross-language Information Retrieval (CLIR) a challenging task. Moreover, in agglutinative languages which do not have reliable stemmers, miss-ing various lexical formations in bilingual dictionaries degrades CLIR performance. This paper aims to introduce a probabilistic translation model to solve the ambiguity problem, and also to provide most likely formations of a dictionary candidate. We propose Minimum Edit Sup-port Candidates (MESC) method that exploits a monolingual corpus and a bilingual dictionary to translate users ’ native language queries to documents ’ language. Our experiments show that the proposed method outperforms state-of-the-art dictionary-based English-Persian CLIR.