Generating Chinese named entity data from parallel corpora

Abstract

Annotating named entity recognition (NER) training corpora is a costly but necessary process for supervised NER approaches. This paper presents a general framework to generate large-scale NER training data from parallel corpora. In our method, we first employ a high performance NER system on one side of a bilingual corpus. Then, we project the named entity (NE) labels to the other side according to the word level alignments. Finally, we propose several strategies to select high-quality auto-labeled NER training data. We apply our approach to Chinese NER using an English-Chinese parallel corpus. Experimental results show that our approach can collect high-quality labeled data and can help improve Chinese NER.

Keywords

named entity recognition / Chinese named entity / training data generating / parallel corpora

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg