A Radical-Based Method for Chinese Named Entity Recognition-Reference-Cited by-同舟云学术

A Radical-Based Method for Chinese Named Entity Recognition

Published:2019 Issue: Volume: Page:
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Container-title:Proceedings of the 2nd International Conference on Big Data Technologies - ICBDT2019
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Author:

Wu Yuefei¹,Wei Xiao¹,Qin Yongbin²,Chen Yanping²

Affiliation:

1. College of Computer Science and Technology, GuiZhou University, Guiyang, China

2. College of Computer Science and Technology, GuiZhou University, Guiyang, China and Guizhou Provincial Key Laboratory of Public Big Data, Guiyang, China

Publisher

ACM Press

Reference13 articles.

1. Duan, H., & Zheng, Y. (2011). A study on features of the CRFs-based Chinese Named Entity Recognition. International Journal of Advanced Intelligence, 3(2), 287--294.

2. Han, A. L. F., Wong, D. F., & Chao, L. S. (2013, June). Chinese named entity recognition with conditional random fields in the light of Chinese characteristics. In Intelligent Information Systems Symposium (pp. 57--68). Springer, Berlin, Heidelberg.

3. Li, L., Mao, T., Huang, D., & Yang, Y. (2006). Hybrid models for Chinese named entity recognition. In Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing (pp. 72--78).

4. Liu, L., Shang, J., Ren, X., Xu, F.F., Gui, H., Peng, J., & Han, J. (2018, April). Empower sequence labeling with task-aware neural language model. In Thirty-Second AAAI Conference on Artificial Intelligence.

5. Peters, M. E., Ammar, W., Bhagavatula, C., & Power, R. (2017). Semi-supervised sequence tagging with bidirectional language models. arXiv preprint arXiv:1705.00108.

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