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Generating Chinese named entity data from parallel corpora

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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.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Ruiji Fu, Bing Qin & Ting Liu

Authors
  1. Ruiji Fu
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  2. Bing Qin
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  3. Ting Liu
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Corresponding author

Correspondence to Ting Liu.

Additional information

Ruiji Fu is a PhD candidate at Harbin Institute of Technology (HIT), China. He received his MS and BS both in Computer Science from HIT, in 2009 and 2007 respectively. His research interests include natural language processing, text mining, and open information extraction.

Bing Qin received her PhD in computer science from Harbin Institute of Technology (HIT), China in 2005. She is a full professor in the School of Computer Science and Technology, HIT. Her research interests include natural language processing, text mining, and opinion mining.

Ting Liu received his PhD in computer science from Harbin Institute of Technology (HIT), China in 1998. He is a full professor in the School of Computer Science and Technology, HIT. His current research interests include natural language processing, information retrieval, and social computing.

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Fu, R., Qin, B. & Liu, T. Generating Chinese named entity data from parallel corpora. Front. Comput. Sci. 8, 629–641 (2014). https://doi.org/10.1007/s11704-014-3127-5

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  • DOI: https://doi.org/10.1007/s11704-014-3127-5

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