LA-MGFM: A legal judgment prediction method via sememe-enhanced graph neural networks and multi-graph fusion mechanism

Abstract

Legal Judgment Prediction (LJP) is a significant task of legal intelligence. Its objective is to predict the relevant law articles, charges, and terms of penalty based on fact descriptions of a criminal case. Existing methods have a drawback: they cannot effectively deal with charges confusion when using various granularity of law articles and predicting outcomes with limited data. In response to this challenge, we propose a solution: a graph neural network-based LJP method that utilizes a multi-graph fusion mechanism to fully and accurately integrate law article information. In detail, we begin by constructing five types of graphs for each case. In the phase of intra-graph information passing, we adopt a Sememe-enhanced Gated Graph Neural Networks to aggregate and update the node features by combining law articles and sememe information. For inter-graph information passing, we introduce a multi-graph fusion mechanism that merges the node features of the five graphs. Finally, we devise a graph readout function, which employs a classifier to derive the results of LJP. The results of our experiment on real-world datasets demonstrate that our method outperforms the current state-of-the-art approaches in our experimental metric.