面向司法案件的案情知识图谱自动构建

以法学知识为中心的认知智能是当前司法人工智能发展的重要方向。该文提出了以自然语言处理(NLP)为核心技术的司法案件案情知识图谱自动构建技术。以预训练模型为基础,对涉及的实体识别和关系抽取这两个NLP基本任务进行了模型研究与设计。针对实体识别任务,对比研究了两种基于预训练的实体识别模型;针对关系抽取任务,该文提出融合平移嵌入的多任务联合的语义关系抽取模型,同时获得了结合上下文的案情知识表示学习。在"机动车交通事故责任纠纷"案由下,和基准模型相比,实体识别的F1值可提升0.36,关系抽取的F1值提升高达2.37。以此为基础,该文设计了司法案件的案情知识图谱自动构建流程,实现了对数十万份判决书案情知识图谱的自动构建,为类案精准推送等司法人工智能应用提供语义支撑。

Automated Knowledge Graph Construction for Judicial Case Facts

Legal knowledge centered cognitive intelligence is an important topic for judicial artificial intelligence. This paper proposes an automated knowledge graph construction approach for judicial case facts. Based on the pre-training model, models for entity recognition and relation extraction are presented. For the entity recognition task, two pre-training based entity recognition models are compared. For the relation extraction task, a multi-task joint semantic relation extraction model is proposed incorporating translating embeddings. The knowledge representation learning of case facts is obtained while completing the relation extraction task. For “motor vehicle traffic accident liability dispute”, compared with the baseline model, the entity recognition can be increased by 0.36 in F₁ score, and the relation extraction by 2.37 F₁ score. Based on the proposed method, a case facts knowledge graphs are established on a couple of hundred thousand judicial documents, enabling the semantic computing for judicial artificial intelligence applications such as case retrieval.