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In Cross-Language Information Retrieval (CLIR), Out-of-Vocabulary (OOV) detection and translation pair relevance evaluation still remain as key problems. In this paper, an English-Chinese Bi-Directional OOV translation model is presented, which utilizes Web mining as the corpus source to collect translation pairs and combines supervised learning to evaluate their association degree. The experimental results show that the proposed model can successfully filter the most possible translation candidate with the lower computational cost, and improve the OOV translation ranking effect, especially for popular new words.

English-Chinese bi-directional OOV translation based on web mining and supervised learning