Bilingual machine readable dictionaries are important and indispensable resources of information for cross-language information retrieval, and machine translation. Recently, these cross-language informational activities have begun to focus on specific academic or technological domains. In this paper, we describe a bilingual dictionary acquisition system which extracts translations from non-parallel but comparable corpora of a specific academic domain and disambiguates the extracted translations. The proposed method is two-fold. At the first stage, candidate terms are extracted from a Japanese and English corpus, respectively, and ranked according to their importance as terms. At the second stage, ambiguous translations are resolved by selecting the target language translation which is the nearest in rank to the source language term. Finally, we evaluate the proposed method in an experiment.
Learning bilingual translations from comparable corpora to cross-language information retrieval
