1. 山西大学计算机与信息技术学院,山西 太原 030006
2. 山西大学计算智能与中文信息处理教育部重点实验室,山西 太原 030006
[ "张虎(1979- ),男,博士,山西大学计算机与信息技术学院副教授,主要研究方向为自然语言处理" ]
[ "潘邦泽(1996- ),男,山西大学计算机与信息技术学院硕士生,主要研究方向为自然语言处理" ]
[ "谭红叶(1971- ),女,博士,山西大学计算机与信息技术学院副教授,主要研究方向为自然语言处理" ]
[ "李茹(1963- ),女,博士,山西大学计算机与信息技术学院教授,主要研究方向为自然语言处理" ]
网络首发:2021-09,
纸质出版:2021-09-15
移动端阅览
张虎, 潘邦泽, 谭红叶, 等. 基于法律裁判文书的法律判决预测[J]. 大数据, 2021,7(5):2021055.
Hu ZHANG, Bangze PAN, Hongye TAN, et al. Legal judgment prediction based on legal judgment documents[J]. Big data research, 2021, 7(5): 2021055.
张虎, 潘邦泽, 谭红叶, 等. 基于法律裁判文书的法律判决预测[J]. 大数据, 2021,7(5):2021055. DOI: 10.11959/j.issn.2096-0271.2021055.
Hu ZHANG, Bangze PAN, Hongye TAN, et al. Legal judgment prediction based on legal judgment documents[J]. Big data research, 2021, 7(5): 2021055. DOI: 10.11959/j.issn.2096-0271.2021055.
针对智慧司法服务领域中“法律判决预测”任务的实际需求,探讨了法律判决预测任务的研究思路与实现路径,介绍了法律判决预测的整体框架和具体过程。基于从中国裁判文书网获取的海量真实案件数据和2018“中国法研杯”司法人工智能挑战赛的评测数据,整理了实验数据类别,规范了实验数据格式,形成了基于法律裁判文书大数据的法律判决预测数据集。在判决预测模型中,首先使用判决要素抽取方法提取出高质量的判决要素句,然后借鉴法官的判案思路,将整个法律判决预测任务转换为法条预测、罪名预测和刑期预测3项子任务,并分别构建了基于判决要素的预测模型。实验结果表明,所提方法在刑法类判决预测数据集上得到了有效的结果。
According to the actual needs of the task of “legal judgment prediction” in the field of intelligent judicial services
the research ideas and implementation ways were discussed
and the overall framework and the specific process of this task were introduced.Based on the massive real cases obtained by China Judgments Online and the evaluation dataset of CAIL2018
the categories were sorted out.The format of the experimental dataset was standardized.And the prediction dataset of legal judgment prediction based on legal judgment documents was built.For the judgment prediction model
the high-quality sentences by using the method of decision elements extraction were extracted.Then refer to the judge’s judgment ideas
the whole task of legal judgment prediction was transform into three subtasks
namely the law articles prediction
the charge prediction
and the penalty prediction.Meanwhile
construct the prediction models based on the judgment elements respectively.The experimental results show that the proposed methods achieves excellent results on the criminal law judgment prediction dataset.
KORT F . Predicting Supreme court decisions mathematically:a quantitative analysis of the “right to counsel” cases [J ] . American Political Science Review , 1957 , 51 ( 1 ): 1 - 12 .
NAGEL S . Applying correlation analysis to case prediction [J ] . Texas Law Review , 1964 , 42 ( 7 ): 1006 - 1017 .
ULMER S S . Quantitative analysis of judicial processes:Some practical and theoretical applications [J ] . Law and Contemporary Problems , 1963 , 28 ( 1 ): 164 .
RINGQUIST E J , EMMERT C E . Judicial policymaking in published and unpublished decisions:the case of environmental civil ligaton [J ] . Political Research Quarterly , 1999 , 52 ( 1 ): 7 - 37 .
LAUDERDALE B E , CLARK T S . The supreme court’s many Median justices [J ] . American Political Science Review , 2012 , 106 ( 4 ): 847 - 866 .
LUO B F , FENG Y S , XU J B , et al . Learning to predict charges for criminal cases with legal basis [C ] // Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing . [S.l]:Association for Computational Linguistics , 2017 : 2727 - 2736 .
JIANG X , YE H , LUO Z C , et al . Interpretable rationale augmented charge prediction system [C ] // Proceedings of the 27th International Conference on Computational Linguistics:System Demonstrations . [S.l]:Association for Computational Linguistics , 2018 : 146 - 151 .
CHEN H J , CAI D , DAI W , et al . Chargebased prison term prediction with deep gating network [C ] // Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing . [S.l]:Association for Computational Linguistics , 2019 : 6362 - 6367 .
HU Z K , LI X , TU C C , et al . Few-shot charge prediction with discriminative legal attributes [C ] // Proceedings of the 27th International Conference on Computational Linguistics .[S.l.:s.n. ] , 2018 : 487 - 498 .
ZHANG H , WANG X , TAN H Y , et al . Applying data discretization to DPCNN for law article prediction [C ] // Proceedings of the 8th CCF International Conference on Natural Language Processing and Chinese Computing . Heidelberg:Springer , 2019 : 459 - 470 .
LONG S B , TU C C , LIU Z Y , et al . Automatic judgment prediction via legal reading comprehension [J ] . arXiv preprint,2018,arXiv:1809.06537 .
YANG W M , JIA W J , ZHOU X J , et al . Legal judgment prediction via multiperspective bi-feedback network [C ] // Proceedings of the 28th International Joint Conference on Artificial Intelligence .[S.l.:s.n. ] , 2019 : 4085 - 4091 .
YANG Z , WANG P F , ZHANG L , et al . A recurrent attention network for judgment prediction [C ] // Proceedings of the 2019 International Conference on Artificial Neural Network . Heidelberg:Springer , 2019 : 253 - 266 .
ZHONG H X , WANG Y Z , TU C C , et al . Iteratively questioning and answering for interpretable legal judgment prediction [C ] // Proceedings of the 2020 AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2020 : 1250 - 1257 .
ZHONG H X , GUO Z P , TU C C , et al . Legal judgment prediction via topological learning [C ] // Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . [S.l.]:Association for Computational Linguistics , 2018 : 3540 - 3549 .
LI J J , ZHANG G Y , YAN H F , et al . A Markov logic networks based method to predict judicial decisions of divorce cases [C ] // Proceedings of 2018 IEEE International Conference on Smart Cloud . Piscataway:IEEE Press , 2018 : 129 - 132 .
LIU Y H , CHEN Y L , HO W L . Predicting associated statutes for legal problems [J ] . Information Processing & Management , 2015 , 51 ( 1 ): 194 - 211 .
ZHONG H X , XIAO C J , TU C C , et al . How does NLP benefit legal system:a summary of legal artificial intelligence [C ] // Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . [S.l.]:Association for Computational Linguistics , 2020 : 5218 - 5230 .
0
浏览量
526
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621