Legal Judgment Prediction via Heterogeneous Graphs and Knowledge of Law Articles
Source abstract
Abstract
Legal judgment prediction (LJP) is a crucial task in legal intelligence to predict charges, law articles and terms of penalties based on case fact description texts. Although existing methods perform well, they still have many shortcomings. First, the existing methods have significant limitations in understanding long documents, especially those based on RNNs and BERT. Secondly, the existing methods are not good at solving the problem of similar charges and do not fully and effectively integrate the information of law articles. To address the above problems, we propose a novel LJP method. Firstly, we improve the model’s comprehension of the whole document based on a graph neural network approach. Then, we design a graph attention network-based law article distinction extractor to distinguish similar law articles. Finally, we design a graph fusion method to fuse heterogeneous graphs of text and external knowledge (law article group distinction information). The experiments show that the method could effectively improve LJP performance. The experimental metrics are superior to the existing state of the art.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Artificial Intelligence in Law · Legal Education and Practice Innovations · Law, AI, and Intellectual Property
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Software College, Northeastern University, Shenyang 110169, China
Neusoft Corporation, Shenyang 110179, China
Funding
National Natural Science Foundation of China · Fundamental Research Funds for the Central Universities
References (34)
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