SIM-GCN: similarity graph convolutional networks for charges prediction

In recent years, the analysis of legal judgments and the prediction of outcomes based on case factual descriptions have become hot research topics in the field of judiciary. Among them, the task of charge prediction aims to predict the applicable charges of a judicial case based on its factual description, making it an important research area in the intelligent judiciary. While significant progress has been made in machine learning and deep learning, traditional methods are limited to handling data in Euclidean space and cannot effectively capture the semantic information in the text. To overcome the limitations of traditional learning approaches, many studies have started exploring the use of graphs to represent rich relationships between entities in text and employing graph convolutional neural networks to learn text representations. In this paper, we propose a charge prediction method based on graph convolutional neural networks. By constructing a similarity graph between cases and utilizing graph convolutional neural networks to learn case feature representations, we can better capture the relational information between cases and improve the accuracy of charge prediction. Experimental results on multiple benchmark datasets demonstrate that our proposed model outperforms traditional methods in charge prediction tasks.