Identifying Relationship of Chinese Characters with Attention Mechanism and Convolutional Neural Network

[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. © 2022, Chinese Academy of Sciences. All rights reserved.