Multi-feature Based Chinese-English Named Entity Extraction from Comparable Corpora

Abstract. Bilingual Named Entity Extraction is important to some cross language information processes such as machine translation (MT), cross-lingual information retrieval (CLIR), etc. A lot of previous work extracted bilingual Named Entities from parallel corpus. Here we propose a multi-feature based method to extract bilingual Named Entities from comparable corpus. We first recognize the Chinese and English Named Entities respectively from the Chinese and English part of the comparable corpus. Then all the feature scores are calculated for every possible pair of Chinese and English Named Entities. At last we combine these feature scores together and decide which pairs are mutual translations. For translation score calculation, we didn’t use the formula of IBM model 1 like previous approach. In stead, we used a modified edit distance to take the order of words into consideration. Experiment shows that the F-score of this method increased by 11 %. And with the multi-feature integration strategy encouraging results are obtained.