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《山东大学学报(理学版)》 ›› 2021, Vol. 56 ›› Issue (11): 24-30.doi: 10.6040/j.issn.1671-9352.1.2020.043

• • 上一篇    

基于BERT与法条知识驱动的法条推荐方法

唐光远1,2,郭军军1,2,余正涛1,2,张亚飞1,2,高盛祥1,2   

  1. 1.昆明理工大学信息工程与自动化学院, 云南 昆明 650500;2.昆明理工大学云南省人工智能重点实验室, 云南 昆明 650500
  • 发布日期:2021-11-15
  • 作者简介:唐光远(1992— ),男,硕士研究生,研究方向为自然语言处理.E-mail:1902547647@qq.com
  • 基金资助:
    国家自然科学基金资助项目(61762056,61866020,61761026,61972186);云南省自然科学基金资助项目(2018FB104)

Method of recommendation based on knowledge driven by BERT and law

TANG Guang-yuan1,2 , GUO Jun-jun1,2 , YU Zheng-tao1,2, ZHANG Ya-fei 1,2,GAO Sheng-xiang1,2   

  1. 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunan, China;
    2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
  • Published:2021-11-15

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摘要： 针对传统法条推荐方法知识利用不足的问题,结合预训练BERT模型,提出了一种基于司法领域法律条文知识驱动的法条推荐方法。首先基于BERT预训练模型对法条知识和案件描述分别进行表征,并基于双向LSTM对案件描述文本进行特征提取,然后基于注意力机制提取融合法条知识的案件描述文本特征,最终实现法条智能推荐。该方法在法研杯公共数据集上,法条推荐F1值达到0.88,结果表明,融合法条知识的BERT模型对法条推荐具有显著提升作用,并且可以有效地解决易混淆法条推荐问题。

关键词: 法条推荐, BERT模型, 法条知识融合, 注意力机制

Abstract: Aiming at the problem of insufficient knowledge utilization of traditional law recommendation methods, this article combines the pre-training BERT(bidirectional encoder representation from transformers)model to propose a law recommendation method based on knowledge-driven legal provisions in the judicial field. First based on the BERT pre-training model, the legal knowledge and the case description are characterized separately, and the case description text is extracted based on the two-way LSTM, and then the case description text features combined with the legal knowledge are extracted based on the attention mechanism, and the intelligent recommendation of the legal clause is finally realized. Using this method on the law research cup public data set, the recommended F1 value of the law can reach 0.88. From this effect, it can be seen that the BERT model fused with knowledge of the law can significantly improve the law recommendation and can effectively solve the easily confused method of recommended questions.

Key words: recommendation of law, BERT model, knowledge fusion of law, attention mechanism

中图分类号: 

  • TP391
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