Abstract
With the continuous progress of digitization in Chinese judicial institutions, a substantial amount of electronic legal document information has been accumulated. To unlock its potential value, entity and relation extraction for legal documents has emerged as a crucial task. However, existing methods often lack domain-specific knowledge and fail to account for the unique characteristics of the judicial domain. In this paper, we propose an entity and relation extraction algorithm based on hypergraph neural network (Legal-KAHRE) for drug-related judgment documents. Firstly, we design a candidate span generator based on neighbor-oriented packing strategy and biaffine mechanism, which identifies spans likely to contain entities. Secondly, we construct a legal dictionary with judicial domain knowledge and integrate it into text encoding representation using multi-head attention. Additionally, we incorporate domain-specific cases like joint crimes and combined punishment for multiple crimes into the hypergraph structure design. Finally, we employ a hypergraph neural network for higher-order inference via message passing. Experimental results on the CAIL2022 information extraction dataset demonstrate that our method significantly outperforms existing baseline models.
Abstract (translated)
随着中国司法机构数字化的不断推进，积累了大量的电子法律文件信息。为了挖掘这些数据的巨大潜力，从法律文档中提取实体和关系成为了一个至关重要的任务。然而，现有的方法通常缺乏特定领域的专业知识，并且未能充分考虑司法领域独有的特点。为此，在本文中我们提出了一种基于超图神经网络（Legal-KAHRE）的药物相关判决文书的实体和关系抽取算法。 首先，我们设计了基于邻居导向打包策略与双线性机制的候选片段生成器，该生成器能够识别可能包含实体的文本片段。其次，我们构建了一个包含司法领域知识的法律词典，并通过多头注意力机制将其集成到文本编码表示中。此外，我们将特定领域的案例如共同犯罪和多项罪行合并处罚纳入超图结构设计之中。 最后，我们利用超图神经网络进行高层次推理，通过消息传递来实现更深层次的理解。在CAIL2022信息抽取数据集上的实验结果显示，我们的方法显著优于现有的基准模型。
URL
https://arxiv.org/abs/2602.08289
