基于超图神经网络的法律文档知识增强实体与关系抽取
Knowledge Augmented Entity and Relation Extraction for Legal Documents with Hypergraph Neural Network
AI总结：
本文提出基于超图神经网络的法律文档实体与关系抽取方法，通过整合领域知识和多头注意力机制，提升司法领域文本处理性能。
摘要：
随着中国司法机构数字化进程的持续发展，大量电子法律文档信息已得以积累。为释放其潜在价值，法律文档的实体与关系抽取已成为关键任务。然而，现有方法往往缺乏领域特定知识，未能考虑司法领域的独特特征。本文提出了一种基于超图神经网络（Legal-KAHRE）的实体与关系抽取算法，用于药品相关判决文档。首先，我们设计了一个基于邻域导向打包策略和双 affine 机制的候选跨度生成器，以识别可能包含实体的跨度。其次，我们构建了一个包含司法领域知识的法律词典，并将其整合到文本编码表示中，使用多头注意力机制。此外，我们将领域特定案例，如共同犯罪和数罪并罚，纳入超图结构设计中。最后，我们采用超图神经网络通过消息传递进行更高阶推断。在CAIL2022信息抽取数据集上的实验结果表明，我们的方法显著优于现有基线模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
