Chinese named entity recognition based on Transformer encoder

被引：0
|
作者：
Guo X.-R. [1 ]
Luo P. [2 ]
Wang W.-L. [3 ]
机构：
[1] School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou
[2] School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou
[3] Key Laboratory of China's Ethnic Languages and Information Technology, Ministry of Education, Northwest Minzu University, Lanzhou
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2021年 / 51卷 / 03期
关键词：
BiLSTM; Named entity recognition; Position coding; Transformer encoder;
D O I：
10.13229/j.cnki.jdxbgxb20200640
中图分类号：
学科分类号：
摘要：
This paper proposes a Chinese named entity recognition method based on Transformer encoder and BiLSTM. This method uses a joint vector as the word representation layer by combining the word embedding and the position coding vector to avoid the losses of the word embedding information and the position information. The directional information is integrated into the joint vector using BiLSTM. The Transformer encoder is introduced to further extract the word relationship features. The experimental results show that the F value of this method on the general MSRA and Thangka domain data sets reaches 81.39% and 86.99% respectively, which effectively improve the effect of Chinese named entity recognition. © 2021, Jilin University Press. All right reserved.
引用
收藏
页码：989 / 995
页数：6
相关论文
共 22 条
  • [1] Zhang Xiao-yan, Wang Ting, Chen Huo-wang, Research on named entity recognition, Computer Science, 32, 4, pp. 44-48, (2005)
  • [2] Liu Liu, Wang Dong-bo, A review on named entity recognition, Journal of the China Society for Scientific and Technical Information, 37, 3, pp. 329-340, (2018)
  • [3] Zhang Yue-jie, Xu Zhi-ting, Xue Xiang-yang, Fusion of multiple features for Chinese named entity recognition based on maximum entropy model, Journal of Computer Research and Development, 45, 6, pp. 1004-1010, (2008)
  • [4] Morwal S, Jahan N, Chopra D., Named entity recognition using hidden Markov model, International Journal on Natural Language Computing, 1, 4, pp. 15-23, (2012)
  • [5] Ju Zhen-fei, Wang Jian, Zhu Fei, Named entity recognition from biomedical text using SVM, International Conference on Bioinformatics & Biomedical Engineering, pp. 1-4, (2011)
  • [6] Wang Lu-lu, Aishan Wumaier, Maihemuti Maimaiti, Et al., A semi-supervised approach to uyghur named entity recognition based on CRF, Journal of Chinese Information Processing, 32, 11, pp. 16-26, (2018)
  • [7] Habibi Maryam, Weber Leon, Mariana Neves, Et al., Deep learning with word embeddings improves biomedical named entity recognition, Bioinformatics, 33, 14, pp. 37-48, (2017)
  • [8] Lei J, Tang B, Lu X, Et al., Research and applications: a comprehensive study of named entity recognition in Chinese clinical text, Journal of the American Medical Informatics Association, 21, 5, pp. 808-814, (2014)
  • [9] Ji Y, Tong C, Liang J, Et al., A deep learning method for named entity recognition in bidding document, Journal of Physics: Conference Series, 1168, 3, (2019)
  • [10] Levy O, Goldberg Y., Neural word embedding asimplicit matrix factorization[J/OL]
123