| 摘要: |
| 法律条文(简称法条)是司法量刑的主要依据,法律条文的精准推荐,能够辅助提高法律智能判决的质量.目前,主流的法条推荐模型是将有限数量的法条当作类别标签,采用分类的思想,根据法律文书的案例描述将其归类到相关的法条.但是法条作为法律规范的文字表述形式,现有的分类方法简单将其作为类别标签的索引编号,导致对其语义信息利用不足,影响了推荐质量.针对此问题,研究将主流的法条推荐方法从分类模型转化为语义匹配模型,提出了基于深度语义匹配的法条推荐方法(DeepLawRec).该方法包含局部语义匹配模块和全局语义推荐模块,分别设计双向Transformer卷积网络模型和基于回归树的推荐模型,在理解文本序列的同时,关注与法条匹配学习相关的局部语义特征,增强法条推荐的准确率和可解释性.在公开数据集上的实验结果表明,DeepLawRec方法在推荐质量上优于传统的文本分类以及经典的语义匹配方法,并进一步探讨了如何分析和判读推荐结果. |
| 关键词: 法律条文推荐 语义匹配 卷积神经网络 可解释性 |
| DOI：10.13328/j.cnki.jos.006287 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金(61876062) |
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| Law Article Recommendation Approach Based on Deep Semantic Matching |
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LI Lin1, DUAN Wei1, ZHOU Dong2, YUAN Jing-Ling1
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1.School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China;2.School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
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| Abstract: |
| Law articles are the main basis of legal judgment and related law article recommendation can improve the quality of legal judgment prediction (LJP). Currently, the state-of-the-art methods belong to supervised classification model with the finite law articles as discrete class labels, which, however, has the downside that the semantic information of law articles may be underused. This observation implies that the quality of law article recommendation can be further enhanced. To solve this problem, this study proposes a deep semantic matching based law article recommendation approach (DeepLawRec) by transforming the traditional classification solution to pairwise matching learning. The proposed DeepLawRec includes local semantic matching module and global semantic recommendation module with a bi-transformer convolutional neural network and regression tree based recommendation, respectively. It can not only extract the key semantic features from the fact descriptions of cases, but also learn their related and local semantic features with a given law article. Moreover, with the help of regression tree, the recommendation results could be interpreted. The experimental results on a public dataset show the proposed DeepLawRec approach can improve the quality of recommendation and outperform the state-of-the-art techniques in terms of precision and interpretability. |
| Key words: legal article recommendation semantic matching convolution neural network interpretability |
