A Charge Prediction Method Based on Graph Attention Network: CP-GAT

The task of charge prediction is to predict the charge of a case based on text data. Aiming at the problem that the existing methods do not perform well on similar charges and long tail datasets, a case charge prediction method was proposed based on graph attention network(CP-GAT). Firstly, the case event description text in the judicial document data set and the corresponding legal information of the case are used to establish the heterogeneous graph structure data. The constructed heterogeneous graph contains two types of nodes(word nodes and case nodes), two types of edges(the edges connected by word nodes and word nodes, the edges connected by word nodes and case nodes). The graph attention network was used to extract graph features on the heterogeneous graph constructed based on texts, and finally the obtained feature vector was input into the classifier of charge prediction to get the charge of the case. The experimental results on the CAIL2018 legal dataset show that the charge prediction method based on graph attention network is better than the model used in the comparative experiment, and the accuracy and macro F1 value reach 95.2% and 66.1 respectively, which verifies that the proposed method is conducive to improving the performance of the case charge prediction task. © 2021, Editorial Department of Journal of Northeastern University. All right reserved.