Abstract
In this paper, we explore the application of cognitive intelligence in legal knowledge, focusing on the development of judicial artificial intelligence. Utilizing natural language processing (NLP) as the core technology, we propose a method for the automatic construction of case knowledge graphs for judicial cases. Our approach centers on two fundamental NLP tasks: entity recognition and relationship extraction. We compare two pre-trained models for entity recognition to establish their efficacy. Additionally, we introduce a multi-task semantic relationship extraction model that incorporates translational embedding, leading to a nuanced contextualized case knowledge representation. Specifically, in a case study involving a "Motor Vehicle Traffic Accident Liability Dispute," our approach significantly outperforms the baseline model. The entity recognition F1 score improved by 0.36, while the relationship extraction F1 score increased by 2.37. Building on these results, we detail the automatic construction process of case knowledge graphs for judicial cases, enabling the assembly of knowledge graphs for hundreds of thousands of judgments. This framework provides robust semantic support for applications of judicial AI, including the precise categorization and recommendation of related cases.
Abstract (translated)
在本文中，我们探讨了在法律知识中的应用认知智能，重点是发展司法人工智能。利用自然语言处理（NLP）作为核心技术，我们提出了一个自动构建案件知识图谱的方法，用于司法案例。我们的方法以两个基本的NLP任务为基础：实体识别和关系提取。我们比较了两个预训练模型，以评估它们的有效性。此外，我们还引入了一个多任务语义关系提取模型，该模型包括翻译嵌入，导致了一种细微的上下文化案例知识表示。具体来说，在涉及"机动车交通事故责任纠纷"的案件研究中，我们的方法显著优于基线模型。实体识别的F1得分提高了0.36，关系提取的F1得分增加了2.37。基于这些结果，我们详细描述了为司法案例自动构建知识图谱的过程，使得知识图谱可以组装成数百万个判决。这个框架为司法人工智能的应用提供了稳健的语义支持，包括对相关案例的准确分类和推荐。
URL
https://arxiv.org/abs/2404.09416
