Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (8): 80-85.doi: 10.11896/jsjkx.210300130

Special Issue: Natural Language Processing

• Database & Big Data & Data Science • Previous Articles     Next Articles

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

WANG Li-mei1, ZHU Xu-guang2,3, WANG De-jia3, ZHANG Yong4 , XING Chun-xiao4   

  1. 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).

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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

CLC Number: 

  • TP391.4
[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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