摘要
【目的】利用解耦技术缓解过度平滑并构建深度图网络学习文本隐藏特征,同时采用注意力扩散机制增强图网络的长距离交互能力,以提升法律文本细粒度分类效果。【方法】提出基于深度注意力扩散图神经网络的法律文本细粒度分类模型FLGNN。首先使用预训练模型BERT作为嵌入层获取长距离语义特征,接着构建文本有向图通过深度图网络捕获文本全局图信息和隐藏特征,最后利用特征融合和节点级注意力机制优化文本特征并进行分类任务。【结果】模型在来自北大法宝数据库的PKULawData数据集上Acc达94.85%,较BERT、DADGNN和RCNN等基线模型分别提升了1.15、3.44和1.72个百分点;在法律合同文本数据集JSCLawData上Acc达90.91%,较BERT、DADGNN和RCNN等基线模型分别提升了1.35、4.19和4.10个百分点。【局限】模型在其他领域的适用性需要进一步探究。【结论】FLGNN模型能捕获法律文本的全局图信息并挖掘深层语义信息,进一步提升了法律文本细粒度分类效果,可为法律领域智能化管理和人工智能提供有效支撑。
[Objective]This paper aims to improve the effect of fine-grained classification of legal text by using decoupling technique to alleviate excessive smoothing and constructing deep graph network to learn hidden features of text,and meanwhile,we adopt attention diffusion mechanism to enhance the long-distance interaction ability of graph network.[Methods]In this study,we propose a fine-grained legal document classification model FLGNN based on a Deep Attention Diffusion Graph Neural Network,which first uses a pre-trained model BERT as an embedding layer to obtain long-range semantic features,then constructs a text-directed graph to capture the text global graph information and hidden features through a deep graph network,and finally optimizes the textual features and carries out the classification task by utilizing feature fusion and node-level attention mechanisms.[Results]The model has an Acc value of 94.85%on the dataset PKULawData from the NLM database,which is an improvement of 1.15%,3.44%,and 1.72%over the Acc values of the baseline models such as BERT,DADGNN,and RCNN,respectively;and an Acc value of 90.91%on the dataset of legal contract texts,JSCLawData,which is an improvement over BERT,Acc values of baseline models such as DADGNN and RCNN by 1.35%,4.19%and 4.10%respectively.[Limitations]Further exploration of the model’s applicability to other domains is needed.[Conclusions]The FLGNN model can capture the global graph information of the legal texts and mine the semantic information of the deep network,effectively improving the classification effect of fine-grained legal texts and providing practical support for artificial intelligence in the legal domain.
作者
韩普
王志伟
杜文文
张子豪
Han Pu;Wang Zhiwei;Du Wenwen;Zhang Zihao(School of Management,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Key Laboratory of Data Engineering and Knowledge Services in Jiangsu Province(Nanjing University),Nanjing 210023,China)
出处
《数据分析与知识发现》
北大核心
2025年第4期134-144,共11页
Data Analysis and Knowledge Discovery
基金
江苏高校青蓝工程
国家级大学生创新创业训练项目(项目编号:202310293048Z)的研究成果之一。
关键词
法律文本分类
深度图神经网络
细粒度分类
解耦合
注意力扩散
Legal Text Classification
Deep Graph Neural Networks
Fine-Grained Classification
Decouple
Attention Diffusion
作者简介
通讯作者:韩普,ORCID:0000-0001-5867-4292,E-mail:hanpu@njupt.edu.cn。
