Generating Chinese Named Entity Data from a Parallel Corpus

Annotating Named Entity Recognition (NER) training corpora is a costly process but necessary for supervised NER systems. This paper presents an approach to generate large-scale Chinese NER training data from an EnglishChinese discourse level aligned parallel corpus. Difficulty of NER is different among languages due to their unique features. For example, the performance of English NER systems is usually higher than the Chinese ones on average. In our method, we first employ a high performance NER system on one side of a bilingual corpus. And then, we project the NE labels to the other side according to the word level alignment. At last, we select high-quality labeled sentences using different strategies and generate an NER training corpus. In our experiments, we generate a Chinese NER corpus with 167,100 sentences through an EnglishChinese parallel corpus. The system trained on the automatically generated corpus attains a comparable result with the one trained on the manuallyannotated corpus. Further experiments show that the NER performance is significantly improved on two different evaluation sets by using the generated training data as an additional corpus to the manually-labeled data.