Abstract：Named entity translation equivalents play a critical role in cross-language information processing. The traditional method is usually based on large-scale parallel or comparable corpus, which is limited by the size and quality of the corpus resources. In Japanese-Chinese translation, the bilingual corpora resources are relatively scarce: the Chinese Hanzi and Japanese Kanji mapping table is often adopted to deal with Chinese named entity and a SMT model to deal with the Japanese named entities in pure kana. In this paper, we propose a monolingual corpora based approach. Firstly, the conditional random field model is adopted to extract Japanese and Chinese names from monolingual corpus. Then the Japanese-Chinese transliteration rule base is developed by instance based inductive learning in a iterative process employing the feedback learning. Experimental results show that the proposed method is simple and efficient, leverging the severely dependency on bilingual resource by the classical methods.
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2015, Vol. 29 
