基于知识融合和依赖性掩盖的法律判断预测模型
Yishan Chen1, Xiaoyi Zhu2, Zhiyun Zeng3
1School of Business, Guilin Tourism University, Guilin, China.
PloS one
|January 16, 2026
概括
这项研究引入了一种新的法律判断预测 (LJP) 模型,该模型增强了司法知识融合和依赖性掩盖,以获得更准确的法律AI预测. 该模型可以提高对法律文件的理解,并过错误的信息以获得更好的结果.
科学领域:
- 人工智能的人工智能
- 法律AI 法律AI
- 自然语言处理自然语言处理.
背景情况:
- 在民法系统中,用于法律判断预测 (LJP) 的现有深度学习模型难以在多任务框架中整合外部司法知识和管理依赖信息.
- 局限性包括对法律文件的深入理解不足和对错误的依赖数据的过不有效.
研究的目的:
- 开发一个先进的法律判断预测模型,通过有效地融合司法知识和掩盖错误的依赖信息来克服当前的局限性.
- 提高人工智能系统在预测法律结果 (如法律条款,指控和处罚条款) 的准确性和可靠性.
主要方法:
- 集成基于CNN的本地语义精细化组件与基于BERT的法律知识提取方法,以更好地从司法文件中提取核心知识.
- 引入差异性注意力机制,以减少注意力融合中的噪音,并改善案例事实中关键信息的准确定位.
- 开发一个多任务依赖信息掩盖机制,以精确识别和过多任务LJP框架中的错误依赖信息.
主要成果:
- 与现有方法相比,拟议的模型在真实世界数据集上表现出优异的性能.
- 提高从司法文件中提取和利用核心法律知识的能力.
- 在识别和过错误的依赖信息方面提高了准确性,从而导致更可靠的预测.
结论:
- 新的知识融合和依赖性掩盖方法显著提升了法律判断预测模型的功能.
- 开发的模型为法律人工智能系统提供了更强大,更准确的解决方案,解决了司法知识整合和信息过方面的关键挑战.
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