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- W4384162941 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." @default.
- W4384162941 created "2023-07-14" @default.
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- W4384162941 date "2023-09-01" @default.
- W4384162941 modified "2023-09-23" @default.
- W4384162941 title "LA-MGFM: A legal judgment prediction method via sememe-enhanced graph neural networks and multi-graph fusion mechanism" @default.
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- W4384162941 doi "https://doi.org/10.1016/j.ipm.2023.103455" @default.
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