Hubei University of Science and Technology, Xianning, China.
Nanjing University of Information Science and Technology, Nanjing, China.
Comput Intell Neurosci. 2022 Sep 14;2022:6313161. doi: 10.1155/2022/6313161. eCollection 2022.
Smart court technologies are making full use of modern science to promote the modernization of the trial system and trial capabilities, for example, artificial intelligence, Internet of things, and cloud computing. The smart court technologies can improve the efficiency of case handling and achieving convenience for the people. Article recommendation is an important part of intelligent trial. For ordinary people without legal background, the traditional information retrieval system that searches laws and regulations based on keywords is not applicable because they do not have the ability to extract professional legal vocabulary from complex case processes. This paper proposes a law recommendation framework, called LawRec, based on Bidirectional Encoder Representation from Transformers (BERT) and Skip-Recurrent Neural Network (Skip-RNN) models. It intends to integrate the knowledge of legal provisions with the case description and uses the BERT model to learn the case description text and legal knowledge, respectively. At last, laws and regulations for cases can be recommended. Experiment results show that the proposed LawRec can achieve better performance than state-of-the-art methods.
智能法庭技术正在充分利用现代科学,促进审判系统和审判能力的现代化,例如人工智能、物联网和云计算。智能法庭技术可以提高案件处理的效率,为人民提供便利。文章推荐是智能审判的一个重要组成部分。对于没有法律背景的普通人来说,传统的基于关键词搜索法律法规的信息检索系统并不适用,因为他们没有从复杂的案件处理过程中提取专业法律词汇的能力。本文提出了一种基于双向编码器表示从变换(BERT)和跳过递归神经网络(Skip-RNN)模型的法律推荐框架,称为 LawRec。它旨在将法律条款的知识与案件描述相结合,并使用 BERT 模型分别学习案件描述文本和法律知识。最后,可以为案件推荐法律法规。实验结果表明,所提出的 LawRec 可以实现比最先进方法更好的性能。