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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (8): 41-51    DOI: 10.11925/infotech.2096-3467.2021.1079
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Identifying Relationship of Chinese Characters with Attention Mechanism and Convolutional Neural Network
Zhao Pengwu1,Li Zhiyi2(),Lin Xiaoqi2
1Institute of Scientific and Technical Information of China, Beijing 100038, China
2School of Economics & Management, South China Normal University, Guangzhou 510006, China
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Abstract  

[Objective] The paper tries to identify the features and relationship of dynamic semantic information from the Chinese character entities. [Methods] First, we used the attention mechanism and improved convolution neural network model to automatically extract features from the training data of public corpus with character entity relationship. Then, we compared our model’s performance with the existing ones from the perspectives of entity relationship recognition efficiency, as well as entity relationship extraction effects and efficiency. [Results] The performance of CNN+Attention model is better than those of the SVM, LR, LSTM, BiLSTM and CNN model in prediction accuracy. Our new model is 0.92% higher in accuracy, 0.80% higher in recall and 0.86% higher in F1 value than the BiLSTM model with relatively better extraction effect. [Limitations] We need to examine our model with more sample data sets. [Conclusions] The proposed model could effectively improve the accuracy and recall of entity relationship extraction for Chinese characters.

Key wordsConvolutional Neural Network      Attention Mechanism      Chinese Character      Relationship Extraction      Relationship Recognition     
Received: 22 September 2021      Published: 23 September 2022
ZTFLH:  N99  
Fund:National Social Science Fund of China(17BTQ062)
Corresponding Authors: Li Zhiyi,ORCID: 0000-0001-6407-2554     E-mail: leeds@scnu.edu.cn

Cite this article:

Zhao Pengwu, Li Zhiyi, Lin Xiaoqi. Identifying Relationship of Chinese Characters with Attention Mechanism and Convolutional Neural Network. Data Analysis and Knowledge Discovery, 2022, 6(8): 41-51.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1079     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I8/41

CNN+Attention Model Structure
关系 ID
Unknown 0
父母 1
合作 2
师生 3
投资 4
对手 5
情侣 6
雇佣 7
朋友 8
亲戚 9
同学 10
上下级 11
The Relationship and Corresponding ID
Flow Chart of Research Scheme
属性 属性值
Keras版本 2.2.4
TensorFlow版本 1.10
GPU NVIDIA GeForce GTX 1080Ti
CPU AMD 2950X@3.50GHz
内存 32GB
操作环境 Linux,Ubuntu16.05
Experimental Environment
参数名称 参数值
Embedding维度 200
Epochs 250
Batch size 256
Dropout rate 0.3
Learning rate 0.001
Parameter Settings of Entity Relationship Extraction Experiment
预测 实际有关系 实际无关系
预测有关系 True Positive(TP False Positive(FP
预测无关系 False Negative(FN True Negative(TN
P-R Confusion Matrix
模型 P R F1
SVM 0.901 8 0.874 3 0.887 8
LR 0.783 3 0.764 2 0.773 6
LSTM 0.938 4 0.935 3 0.936 7
BiLSTM 0.940 8 0.932 1 0.936 3
CNN 0.926 4 0.926 4 0.864 1
CNN+Attention 0.950 0 0.940 1 0.944 9
Experimental Results of Machine Learning Models
关系 P R F1
父母 0.941 4 0.946 3 0.944 1
合作 0.957 6 0.960 2 0.959 0
师生 0.943 8 0.923 5 0.933 6
投资 0.953 9 0.933 6 0.943 6
对手 0.952 8 0.921 4 0.936 8
情侣 0.942 0 0.936 3 0.939 1
雇佣 0.965 0 0.915 3 0.939 5
朋友 0.949 2 0.880 9 0.913 8
亲戚 0.911 2 0.868 4 0.889 3
同学 0.973 3 0.921 9 0.929 7
上下级 0.971 1 0.950 5 0.960 7
CNN+Attention Entity Relationship Extraction Effect
Accuracy and Loss Rate Changes of CNN+Attention Model
向量 P R F1
CNN+Attention(随机初始化向量) 0.876 1 0.848 3 0.861 9
CNN+Attention(经训练的向量) 0.917 6 0.887 5 0.902 1
Randomization Vector and Rraining Vector Relationship Extraction Effect
PR Diagram of Entity Relationship Extraction
Average Accuracy of CNN+Attention Model
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