结合案件要素序列的罪名预测方法

doi:10.11959/j.issn.2096-0271.2021058

Abstract

Abstract:

Charge prediction is to find the appropriate charges based on the facts of the given case.Existing methods mainly use text content for classification, but they cannot effectively use case elements.For the shortcomings of the existing methods, the method of accusation prediction based on the sequence of case elements was put forward.The way expressed the case factual processes as a series of case elements with “behavior” as the core and time-series relationship.Then graph convolutional network was used to represent.Finally, the semantic features of the text were fused to predict the crime.Experiments show that this method has better prediction performance than existing methods.Meanwhile, this method also has a good performance for the distinction between easily confusing charges.

Key words: facts of the case, graph convolutional network, case element, text classification

CLC Number:

TP391.1

Qian SUN, Yongbin QIN, Ruizhang HUANG, Lijuan LIU, Yanping CHEN. Charge prediction method combined with case elements sequence[J]. Big Data Research, 2021, 7(6): 30-40.

Figures/Tables 5

References 24

[1]	秦永彬, 冯丽, 陈艳平 ,等. “智慧法院”数据融合分析与集成应用[J]. 大数据, 2019,5(3): 35-46.
	QIN Y B , FENG L , CHEN Y P ,et al. “Intelligent Court” data fusion analysis and integrated application[J]. Big Data Research, 2019,5(3): 35-46.
[2]	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.
[3]	NAGEL S S . Applying correlation analysis to case prediction[J]. Texas Law Review, 1963,42: 10061.
[4]	LIN W C , KUO T T , CHANG T J ,et al. Exploiting machine learning models for Chinese legal documents labeling,case classification,and sentencing prediction[C]// Proceedings of the 24th Conference on Computational Linguistics and Speech Processing.[S.l.:s.n.], 2012:140.
[5]	LIU Y H , CHEN Y L , HO W L . Predicting associated statutes for legal problems[J]. Information Processing ＆ Management, 2015,51(1): 194-211.
[6]	JIANG X , YE H , LUO Z ,et al. Interpretable rationale augmented charge prediction system[C]// Proceedings of the 27th International Conference on Computational Linguistics:System Demonstrations.[S.l.:s.n.], 2018: 146-151.
[7]	KANG L Y , LIU J , LIU L Q ,et al. Creating auxiliary representations from charge definitions for criminal charge prediction[J]. arXiv preprint,2019,arXiv:1911.05202.
[8]	YANG X T , SHI G Z , LOU J P ,et al. Interpretable charge prediction with multi-perspective jointly learning model[C]// Proceedings of 2019 IEEE 5th International Conference on Computer and Communications. Piscataway:IEEE Press, 2019: 1850-1855.
[9]	刘宗林, 张梅山, 甄冉冉 ,等. 融入罪名关键词的法律判决预测多任务学习模型[J]. 清华大学学报(自然科学版), 2019,59(7): 497-504.
	LIU Z L , ZHANG M S , ZHEN R R ,et al. Multi-task learning model for legal judgment predictions with charge keywords[J]. Journal of Tsinghua University (Science and Technology), 2019,59(7): 497-504.
[10]	LUO B F , FENG Y S , XU J B ,et al. Learning to predict charges for criminal cases with legal basis[J]. arXiv preprint,2017,arXiv:1707.09168.
[11]	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.
[12]	HE C Q , PENG L , LE Y Q ,et al. SECaps:a sequence enhanced capsule model for charge prediction[C]// Proceedings of the 2019 International Conference on Artificial Neural Networks. Cham:Springer, 2019: 227-239.
[13]	敖绍林, 秦永彬, 黄瑞章 ,等. 基于卷积神经网络的辅助分案方法研究[J]. 大数据, 2021:已录用.
	AO S L , QIN Y B , HUANG R Z ,et al. Research on auxiliary division method based on convolutional neural network[J]. Big Data Research, 2021:accepted.
[14]	黄辉, 秦永彬, 陈艳平 ,等. 基于BERT阅读理解框架的司法要素抽取方法[J]. 大数据, 2021,7(6): 19-29.
	HUANG H , QIN Y B , CHEN Y P ,et al. Legal element extraction method based on BERT reading comprehension framework[J]. Big Data Research, 2021,7(6): 19-29.
[15]	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): 164-175.
	ZHANG H , PAN B Z , TAN H Y ,et al. Legal judgment prediction based on legal judgment documents[J]. Big Data Research, 2021,7(5): 164-175.
[16]	YAO L , MAO C S , LUO Y . Graph convolutional networks for text classification[C]// Proceedings of the AAAI Conference on Artificial Intelligence.[S.l.:s.n.], 2019,33: 7370-7377.
[17]	ZHANG Y F , YU X L , CUI Z Y ,et al. Every document owns its structure:inductive text classification via graph neural networks[J]. arXiv preprint,2020,arXiv:2004.13826.
[18]	HU L M , YANG T C , SHI C ,et al. Heterogeneous graph attention networks for semi-supervised short text classification[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2019: 4823-4832.
[19]	HUANG L Z , MA D H , LI S J ,et al. Text level graph neural network for text classification[J]. arXiv preprint,2019,arXiv:1910.02356.
[20]	XU N , WANG P H , CHEN L ,et al. Distinguish confusing law articles for legal judgment prediction[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:Association for Computational Linguistics, 2020.
[21]	DEVLIN J , CHANG M W , LEE K ,et al. BERT:pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint,2018,arXiv:1810.04805.
[22]	KIM Y . Convolutional neural networks for sentence classification[J]. arXiv preprint,2014,arXiv:1408.5882.
[23]	李婷, 秦永彬, 黄瑞章 ,等. 基于神经网络的中文谓语动词识别研究[J]. 数据采集与处理, 2020,35(3): 582-590.
	LI T , QIN Y B , HUANG R Z ,et al. Research on Chinese predicate verb recognition based on neural network[J]. Journal of Data Acquisition and Processing, 2020,35(3): 582-590.
[24]	陈艳平, 冯丽, 秦永彬 ,等. 一种基于深度神经网络的句法要素识别方法[J]. 山东大学学报(工学版), 2020,50(2): 44-49.
	CHEN Y P , FENG L , QIN Y B ,et al. A syntactic element recognition method based on deep neural network[J]. Journal of Shandong University (Engineering Science), 2020,50(2): 44-49.

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模型	精确率	召回率	F1(macro)
TFIDF_SVM	81.22%	76.32%	77.41%
TextRNN	94.84%	93.87%	94.29%
FastText	94.62%	94.41%	94.50%
TextCNN	95.07%	95.08%	95.07%
TextRNN_seq	95.38%	94.63%	94.97%
TextCNN_seq	96.30%	95.74%	95.99%

模型	精确率	召回率	F1(macro)
TextCNN	95.07%	95.08%	95.07%
TextCNN_seq	96.30%	95.74%	95.99%
TextCNN_seq_nopre	94.97%	94.04%	94.44%

模型	精确率	召回率	F1(macro)
SVM	90.38%	90.36%	90.37%
FastTest	97.54%	87.49%	97.51%
TextCNN	97.83%	97.81%	97.83%
TextCNN_seq	98.59%	98.62%	98.60%

Charge prediction method combined with case elements sequence

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