Computer Science ›› 2019, Vol. 46 ›› Issue (9): 211-215.doi: 10.11896/j.issn.1002-137X.2019.09.031

• Artificial Intelligence • Previous Articles     Next Articles

Law Article Prediction Method for Legal Judgment Documents

ZHANG Hu1, WANG Xin1, WANG Chong1, CHENG Hao1, TAN Hong-ye1, LI Ru1,2   

  1. (School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China)1;
    (Key Laboratory of Computing Intelligence and Chinese Information Processing,Ministry of Education,Shanxi University,Taiyuan 030006,China)2
  • Received:2018-08-29 Online:2019-09-15 Published:2019-09-02

Abstract: Inrecent years,the analysis of legal judgment documents and the prediction of results based on case facts in the judicial field have become the hot research topics in AI law.The law article prediction task is based on the factual description of the judicial case to predict the applicable law of the cases,which has become an important research content of wisdom justice.After analyzing the factual description of the legal documents and the specific judicial interpretation of the law,and excavating the characteristics of the factual description part of the judicial document,a method of recommending the law based on multi-model fusion was proposed.Based on the public dataset in the “CAIL2018” Judicial Artificial Intelligence Challenge,three datasets were constructed from different angles,and multiple sets of experiments were performed on each dataset.The experimental results show that the proposed method is simpler than the single model of law article prediction.The proposed method can effectively improve the accuracy of the task,and can better solve the recommendation problem of multiple cases in a single case fact description.

Key words: Judgment documents, Law article prediction, Wisdom justice, Model fusion

CLC Number: 

  • TP391
[1]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[J].arXiv:1301.3781,2013.
[2]KALCHBRENNER N,GREFENSTETTE E,BLUNSOM P.A convolutional neural network for modelling sentences[J].arXiv:1404.2188,2014.
[3]KIM Y.Convolutional neural networks for sentence classification[J].arXiv:1408.5882,2014.
[4]TANG D,QIN B,LIU T.Document modeling with gated recurrent neural network for sentiment classification[C]//Procee-dings of the 2015 Conference on Empirical Methods in Natural Language Processing.Lisbon,Portugal:Association for Computational Linguistics,2015:1422-1432.
[5]YANG Z,YANG D,DYER C,et al.Hierarchical attention networks for document classification[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.San Diego,California:Association for Computational Linguistics,2016:1480-1489.
[6]LAUDERDALE B E,CLARK T S.The Supreme Court’s Many Median Justices[J].American Political Science Review,2012,106(4):847-866.
[7]SEGAL J A.Predicting Supreme Court cases probabilistically:The search and seizure cases,1962-1981[J].American Political Science Review,1984,78(4):891-900.
[8]LIU Y H,CHEN Y L.A two-phase sentiment analysis approach for judgement prediction[J].Journal of Information Science,2018,44(5):594-607.
[9]SULEA O M,ZAMPIERI M,MALMASI S,et al.Exploring the use of text classification in the legal domain[J].arXiv:1710.09306,2017.
[10]ALETRAS N,TSARAPATSANIS D,PREOTIUC-PIETRO D,et al.Predicting judicial decisions of the European Court of Human Rights:A natural language processing perspective[J].PeerJ Computer Science,2016,2:e93.
[11]LIU C L,HSIEH C D.Exploring phrase-based classification ofjudicial documents for criminal charges in chinese[C]//International Symposium on Methodologies for Intelligent Systems.Berlin:Springer,2006:681-690.
[12]YE H,JIANG X,LUO Z,et al.Interpretable charge predictions for criminal cases:Learning to generate court views from fact descriptions[J].arXiv:1802.08504,2018.
[13]LUO B,FENG Y,XU J,et al.Learning to predict charges for criminal cases with legal basis[J].arXiv:1707.09168,2017.
[14]LIU C L,LIAO T M.Classifying criminal charges in chinese for web-based legal services[C]//Asia-Pacific Web Conference.Berlin:Springer,2005:64-75.
[15]LIU Y H,CHEN Y L,HO W L.Predicting associated statutes for legal problems[J].Information Processing & Management,2015,51(1):194-211.
[16]ZHANG X,ZHAO J,LECUN Y.Character-level convolutional networks for text classification[M]//Advances in Neural Information Processing Systems.Berlin:Springer,2015:649-657.
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