Generating Chinese named entity data from parallel corpora  

作　　者：Ruiji FU Bing QIN Ting LIU 

机构地区：[1]School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

出　　处：《Frontiers of Computer Science》2014年第4期629-641,共13页中国计算机科学前沿（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China （Grant Nos. 61133012, 61273321） and the National 863 Leading Technology Research Project （2012AA011102）. Special thanks to Wanxiang Che, Yanyan Zhao, Wei He, Fikadu Gemechu, Yuhang Guo, Zhenghua Li, Meishan Zhang and the anonymous reviewers for insightful comments and suggestions.

摘　　要：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.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.

关 键 词：named entity recognition Chinese named entity training data generating parallel corpora 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP391.2[自动化与计算机技术—计算机科学与技术]