Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (3): 989-995.doi: 10.13229/j.cnki.jdxbgxb20200640

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Chinese named entity recognition based on Transformer encoder

Xiao-ran GUO1(),Ping LUO2,Wei-lan WANG3   

  1. 1.School of Mathematics and Computer Science，Northwest Minzu University，Lanzhou 730030，China
    2.School of Electronic and Information Engineering，Lanzhou Jiaotong University，Lanzhou 730070，China
    3.Key Laboratory of China's Ethnic Languages and Information Technology，Ministry of Education，Northwest Minzu University，Lanzhou 730030，China
  • Received:2020-08-20 Online:2021-05-01 Published:2021-05-07

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Abstract:

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.

Key words: named entity recognition, Transformer encoder, BiLSTM, position coding

CLC Number: 

  • TP391

Fig.1

Bilstm transformer CRF model"

Fig.2

LSTM unit"

Fig.3

Transformer coding layer"

Table 1

Statistical information of experimentaldata set"

语料类别训练集验证集测试集
MSRA句子32 4218 1054 631
PER11 2742 8181 973
LOC23 3725 8422 877
ORG13 1663 2911 331
唐卡句子1 591397692
NSL3 1547881 056

Table 2

Comparison of recognition effects ofdifferent models on dataset"

CoupsModelPRF1
MSRACRF52.9631.8939.81
BiLSTM-CRF81.2476.3678.73
BiLSTM-Attention-CRF83.9774.6279.02
Transformer-CRF66.7060.3363.35
Transformer*-CRF67.3161.3564.19
Ours*81.8379.0180.40
Ours87.0576.4381.39
唐卡CRF80.3368.9074.18
BiLSTM-CRF87.5680.0283.63
BiLSTM-Attention-CRF87.6780.7884.09
Transformer-CRF80.3663.9271.20
Transformer*-CRF90.1581.4485.57
Ours*90.1580.6885.15
Ours93.8681.0686.99

Table 3

Identification results of model1 andmodel2 in Thangka test set"

命名实体长度总计
123~56~10>10
测试集实体个数0559306921056
Model1识别实体个数5446821520973
Model1识别正确实体个数028774510853
Model1正确率/%060.894.298.1-87.7
Model2识别实体个数1151797521912
Model2识别正确实体个数033773500856
Model2正确率/%064.796.996.1093.9
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