A Method to Construct Chinese-Japanese Named Entity Translation Equivalents Using Monolingual Corpora

The traditional method of Named Entity Translation Equivalents extraction is often based on large-scale parallel or comparable corpora. But the practicability of the research results is constrained by the relatively scarce of the bilingual corpus resources. We combined the features of Chinese and Japanese, and proposed a method to automatically extract the Chinese-Japanese NE translation equivalents based on inductive learning from monolingual corpus. This method uses the Chinese Hanzi and Japanese Kanji comparison table to calculate NE instances similarity between Japanese and Chinese. Then, we use inductive learning method to obtain partial translation rules of NEs through extracting the differences between Chinese and Japanese high similarity NE instances. In the end, the feedback process refreshes the Chinese and Japanese NE similarity and translation rule sets. Experimental results show that the proposed method is simple and efficient, which overcome the shortcoming that the traditional methods have a dependency on bilingual resource.