基于深度学习的民事案件判决结果分类方法研究

doi:10.11896/jsjkx.210300130

计算机科学 ›› 2021, Vol. 48 ›› Issue (8): 80-85.doi: 10.11896/jsjkx.210300130

所属专题：自然语言处理虚拟专题

• 数据库&大数据&数据科学* • 上一篇下一篇

基于深度学习的民事案件判决结果分类方法研究

王立梅¹, 朱旭光^2,3, 汪德嘉³, 张勇⁴, 邢春晓⁴

1 中国政法大学刑事司法学院北京100088
2 中国政法大学网络法学研究院北京 100088
3 江苏通付盾科技有限公司江苏苏州215000
4 清华大学北京信息科学与技术国家研究中心北京100084

收稿日期:2021-03-12 修回日期:2021-05-21 发布日期:2021-08-10
通讯作者: 朱旭光(xuguangs@yeah.net)
基金资助:
国家重点研发计划(2018YFC0831202)

Study on Judicial Data Classification Method Based on Natural Language Processing Technologies

WANG Li-mei¹, ZHU Xu-guang^2,3, WANG De-jia³, ZHANG Yong⁴, XING Chun-xiao⁴

1 School of Criminal Justice,China University of Political Science and Law,Beijing 100088,China;
2 Institute of Cyber Law,China University of Political Science and Law,Beijing 100088,China;
3 Jiangsu PayEgis Technology Co.,Ltd.,Suzhou,Jiangsu 215000,China;
4 Beijing National Research Center for Information Science and Technology,Tsinghua University,Beijing 100084,China

Received:2021-03-12 Revised:2021-05-21 Published:2021-08-10
About author:WANG Li-mei,born in 1974,Ph.D,professor.Her main research interests include cyber law and so on.(limeiw@cupl.edu.cn)ZHU Xu-guang,born in 1989,master.His main research interests include artificial intelligence and cyber law.
Supported by:
National Key R&D Program(2018YFC0831202).

摘要/Abstract

摘要： 裁判文书数量的快速增长对自动化分类提出了迫切要求,然而已有研究缺乏在民事案件这一细分领域下以判决结果为分类标准的方法的研究,无法实现对民事案件判决结果的准确分类。文中将深度学习技术应用于民事案件判决结果分类领域,通过横向对比多种深度学习模型得出了该领域下表现较好的模型,并依据裁判文书的数据特点对该模型进行了进一步的优化。实验结果证明,Transformer模型的判决结果分类的宏精准率、宏召回率和宏F1分数均高于其他模型。通过对数据预处理流程的优化和对Transformer模型位置嵌入方式的优化,模型的性能指标提升了1%～2%。

关键词: 裁判文书, 大数据, 分类, 深度学习, 司法数据, 自然语言处理

Abstract: The rapid increase in the number of judgment documents puts forward an urgent need for automated classification.However,there is a lack of method in existing studies that use judgment results as the subject of classification in the subdivision of civil cases,and therefore they cannot achieve accurate classification of judgment results in civil cases.In this paper,we apply deep learning technology in the field of classification of judgment results of civil cases,and obtain a model with better perfor-mance in this field through horizontal comparison of multiple deep learning models.This model is further optimized based on the data characteristics of the judgment document.After experiments,the Transformer model's macro precision rate,macro recall rate and macro F1 score in the judgment result classification are all higher than other models.By adjusting the data preprocessing process and adjusting the position embedding method of the Transformer model,the performance index of the model is increased by 1％～2%.

Key words: Big data, Classification, Deep learning, Judgment documents, Judicial data, Natural language processing

中图分类号:

TP391.4

王立梅, 朱旭光, 汪德嘉, 张勇, 邢春晓. 基于深度学习的民事案件判决结果分类方法研究[J]. 计算机科学, 2021, 48(8): 80-85. https://doi.org/10.11896/jsjkx.210300130

WANG Li-mei, ZHU Xu-guang, WANG De-jia, ZHANG Yong, XING Chun-xiao. Study on Judicial Data Classification Method Based on Natural Language Processing Technologies[J]. Computer Science, 2021, 48(8): 80-85. https://doi.org/10.11896/jsjkx.210300130

参考文献

[1]WANG L S.China's experience in the construction of smart courts and its path optimization－Based on the application of big data and artificial intelligence[J].Inner Mongolia Social Sciences,2021,42(1):104-114.
[2]TANG Z,WANG Z S,ZHOU A,et al.Transformer-capsule ensemble model for text classification[J].Computer Engineering and Applications,2020,56(24):151-156.
[3]ZHU Y,GAO X,ZHANG W,et al.A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification[J].Future Internet,2018,10(12):116.
[4]PRASAD K M S,REDDY D T H.Text Mining:Classification of Text Documents using Granular Hybrid Classification Technique[J].International Journal of Research in Advent Technology,2019,7(6):1-8.
[5]BUGUEÑO M,MENDOZA M.Learning to combine classifiers outputs with the transformer for text classification[J].Intelligent Data Analysis,2020,24(1):15-41.
[6]ZHANG A,LI B,WANG W,et al.MII:A Novel Text Classification Model Combining Deep Active Learning with BERT[J].Computers,Materials & Continua,2020,63(3):1499-1514.
[7]CHEN L,SHAH R,LINK T,et al.Bert model fine-tuning for text classification in knee OA radiology reports[J].Osteoarthritis and Cartilage,2020,28(1):S315-S316.
[8]YU Q,WANG Z,JIANG K.Research on Text ClassificationBased on BERT-BiGRU Model[J].Journal of Physics:Confe-rence Series,2021,1746(1):012019.
[9]LEE T S,KANG S S.Automatic Text Summarization Based on Selective OOV Copy Mechanism with BERT Embedding[J].Journal of KIISE,2020,47(1):36-44.
[10]WENG Y,GU S Y,LI J,et al.A Text Classification Algorithm for the Structured Large-scale Judgment Documents[J].Journal of Tianjin University (Natural Science and Engineering Technology Edition),2021,54(4):418-425.
[11]WANG N,LI S L,LIU T L,et al.Tendency analysis of BiGRU decision results based on attention mechanism[J].Computer Systems & Applications,2019,28(3):191-195.
[12]CHENG H.Law Prediction and Similar Case Matching Research Facing Judicial Big Data[D].Taiyuan:Shanxi University,2020.
[13]MOTHUKURI R,BASAVESWARARAO B,SUNEETHA B.Judgement Classification Using Hybrid ANN-Shuffled Frog Leaping Model on Cyber Crime Judgement Database[J].Rev.d'Intelligence Artif,34(4):445-456.
[14]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[C]//Proceedings of the 31st International Confe-rence on Neural Information Processing Systems(NIPS'17).2017:6000-6010.
[15]LI K Y,CHEN Y,NIU S Z.BERT-based social e-commerce text classification algorithm[J].Computer Science,2021,48(2):87-92.
[16]NEBAUER C.Evaluation of convolutional neural networks for visual recognition[J].IEEE Transactions on Neural Networks,1998,9(4):685-696.
[17]WU H Y,YAN J,HUANG S B,et al.CNN_BiLSTM_Attention Hybrid Model for Text Classification[J].Computer Science,2020,47 (11A):24-27.
[18]DU L,CAO D,LIN S Y,et al.Extraction and Automatic Classification of TCM Medical Record Text Based on BERT and Bi-LSTM Fusion Attention Mechanism[J].Computer Science,2020,47 (11A):416-420.

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基于深度学习的民事案件判决结果分类方法研究

Study on Judicial Data Classification Method Based on Natural Language Processing Technologies

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