›› 2018, Vol. 33 ›› Issue (4): 756-767.doi: 10.1007/s11390-018-1854-z

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Recommender Systems with Big Data • Previous Articles     Next Articles

PTM: A Topic Model for the Inferring of the Penalty

Tie-Ke He, Hao Lian, Ze-Min Qin, Zhen-Yu Chen, Bin Luo   

  1. Software Institute, Nanjing University, Nanjing 210093, China;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
  • Received:2017-12-27 Revised:2018-05-08 Online:2018-07-05 Published:2018-07-05
  • About author:Tie-Ke He is a research assistant at Software Institute, Nanjing University, Nanjing. He got his B.S., M.S. and Ph.D. degrees in software engineering from Nanjing University, Nanjing, in 2010, 2012 and 2017, respectively. His research interests include recommender systems and knowledge graph.
  • Supported by:

    This work is supported in part by the National Key Research and Development Program of China under Grant No. 2016YFC0800805 and the National Natural Science Foundation of China under Grant No. 61690201.

Deciding the penalty of a law case has always been a complex process, which may involve with much coordination. Despite the judicial study based on the rules and conditions, artificial intelligence and machine learning has rarely been used to study the problem of penalty inferring, leaving the large amount of law cases as well as various factors among them untouched. This paper aims to incorporate the state-of-the-art artificial intelligence methods to exploit to what extent this problem can be alleviated. We first analyze 145 000 law cases and observe that there are two sorts of labels, temporal labels and spatial labels, which have unique characteristics. Temporal labels and spatial labels tend to converge towards the final penalty, on condition that the cases are of the same category. In light of this, we propose a latent-class probabilistic generative model, namely Penalty Topic Model (PTM), to infer the topic of law cases, and the temporal and spatial patterns of topics embedded in the case judgment. Then, the learnt knowledge is utilized to automatically cluster all cases accordingly in a unified way. We conduct extensive experiments to evaluate the performance of the proposed PTM on a real large-scale dataset of law cases. The experimental results show the superiority of our proposed PTM.

[1] Boyer M, Lewis T R, Liu W L. Setting standards for credible compliance and law enforcement. Canadian Journal of Economics/Revue canadienne d'économique, 2000, 33(2):319-340.

[2] Becker G S. Crime and punishment:An economic approach. The Journal Political Ewnomy, 1968, 76(2):169-217.

[3] Kilgour D M, Fang L, Hipel KW. Game-theoretic analyses of enforcement of environmental laws and regulations. Journal of the American Water Resources Association, 1992, 28(1):141-153.

[4] P'ng I P. Strategic behavior in suit, settlement, and trial. The Bell Journal of Economics, 1983, 14(2):539-550.

[5] Daughety A F, Reinganum J F. Keeping society in the dark:On the admissibility of pretrial negotiations as evidence in court. The RAND Journal of Economics, 1995, 26(2):203-221.

[6] Polinsky A M, Shavell S. Punitive damages:An economic analysis. Harvard Law Review, 1998, 111(4):869-962.

[7] Earnhart D. Enforcement of environmental protection laws under communism and democracy. The Journal of Law and Economics, 1997, 40(2):377-402.

[8] Yin H, Cui B, Sun Y, Hu Z, Chen L. Lcars:A spatial item recommender system. ACM Transactions on Information Systems, 2014, 32(3):Article No. 11.

[9] Yuan Q, Cong G, Ma Z, Sun A, Thalmann N M. Timeaware point-of-interest recommendation. In Proc. the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2013, pp.363-372.

[10] Gao H, Tang J, Hu X, Liu H. Exploring temporal effects for location recommendation on location-based social networks. In Proc. the 7th ACM Conference on Recommender Systems, October 2013, pp.93-100.

[11] Wallach H M, Mimno D M, McCallum A. Rethinking LDA:Why priors matter. Advances in Neural Information Processing Systems, 2009, 23:1973-1981.

[12] Yin H, Sun Y, Cui B, Hu Z, Chen L. LCARS:A locationcontent-aware recommender system. In Proc. the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2013, pp.221-229.

[13] Tang J, Wu S, Sun J, Su H. Cross-domain collaboration recommendation. In Proc. the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2012, pp.1285-1293.

[14] Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3):273-297.

[15] Joulin A, Grave E, Bojanowski P, Mikolov T. Bag of tricks for efficient text classification. In Proc. the 15th Conference of the European Chapter of the Association for Computational Linguistics, April 2017, pp.427-431.

[16] Kim Y. Convolutional neural networks for sentence classification. arXiv:1408.5882. 2014. https://arxiv.org/abs/1408.5882, April 2018.

[17] Herlocker J L, Konstan J A, Terveen L G, Riedl J T. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 2004, 22(1):5-53.

[18] Linden G, Smith B, York J. Amazon.com recommendations:Item-to-item collaborative filtering. IEEE Internet Computing, 2003, 7(1):76-80.

[19] Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. Grouplens:An open architecture for collaborative filtering of netnews. In Proc. the 1994 ACM Conference on Computer Supported Cooperative Work, October 1994, pp.175-186.

[20] Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In Proc. the 10th International Conference on World Wide Web, May 2001, pp.285-295.

[21] Chee S H S, Han J, Wang K. Rectree:An efficient collaborative filtering method. In Proc. the 3rd International Conference on Data Warehousing and Knowledge Discovery, September 2001, pp.141-151.

[22] Chowdhury G. Introduction to Modern Information Retrieval (3rd edition). Facet Publishing, 2010.

[23] Su X, Khoshgoftaar T M, Greiner R. A mixture imputationboosted collaborative filter. In Proc. the 21st International Florida Artificial Intelligence Research Socitey Conference, May 2008, pp.312-316.

[24] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003, 3(Jan):993-1022.

[25] Hofmann T. Probabilistic latent semantic analysis. In Proc. the 15th Conference on Uncertainty in Artificial Intelligence, July 1999, pp.289-296.

[26] Hong L, Ahmed A, Gurumurthy S, Smola A J, Tsioutsiouliklis K. Discovering geographical topics in the twitter stream. In Proc. the 21st International Conference on World Wide Web, April 2012, pp.769-778.

[27] Yuan Q, Cong G, Ma Z, Sun A, Thalmann N M. Who, where, when and what:Discover spatiotemporal topics for twitter users. In Proc. the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2013, pp.605-613.

[28] Wang X, McCallum A. Topics over time:A non-MARKOV continuous-time model of topical trends. In Proc. the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2006, pp.424-433.

[29] Hong L, Yin D, Guo J, Davison B D. Tracking trends:Incorporating term volume into temporal topic models. In Proc. the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2011, pp.484-492.

[30] Yin H, Cui B, Huang Z, Wang W, Wu X, Zhou X. Joint modeling of users' interests and mobility patterns for pointof-interest recommendation. In Proc. the 23rd ACM International Conference on Multimedia, October 2015, pp.819-822.

[31] Eisenstein J, O'Connor B, Smith N A, Xing E P. A latent variable model for geographic lexical variation. In Proc. the 2010 Conference on Empirical Methods in Natural Language Processing, October 2010, pp.1277-1287.

[32] Hu B, Ester M. Spatial topic modeling in online social media for location recommendation. In Proc. the 7th ACM Conference on Recommender Systems, October 2013, pp.25-32.

[33] Yin Z, Cao L, Han J, Zhai C, Huang T. Geographical topic discovery and comparison. In Proc. the 20th International Conference on World Wide Web, March 2011, pp.247-256.

[34] Wang W, Yin H, Chen L, Sun Y, Sadiq S, Zhou X. GeoSAGE:A geographical sparse additive generative model for spatial item recommendation. In Proc. the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2015, pp.1255-1264.

[35] Yin H, Zhou X, Shao Y, Wang H, Sadiq S. Joint modeling of user check-in behaviors for point-of-interest recommendation. In Proc. the 24th ACM International on Conference on Information and Knowledge Management, October 2015, pp.1631-1640.

[36] Yin H, Cui B, Lu H, Huang Y, Yao J. A unified model for stable and temporal topic detection from social media data. In Proc. the 29th International Conference on Data Engineering, April 2013, pp.661-672.

[37] Yin H, Cui B, Chen L, Hu Z, Huang Z. A temporal contextaware model for user behavior modeling in social media systems. In Proc. the 2014 ACM SIGMOD International Conference on Management of Data, June 2014, pp.1543-1554.

[38] Yin H, Cui B, Chen L, Hu Z, Zhou X. Dynamic user modeling in social media systems. ACM Transactions on Information Systems, 2015, 33(3):Article No. 10.

[39] Dau-Schmidt K G, Gallo J, Parker C, Craycraft J. Criminal penalties under the Sherman act:A study of law and economics. Research in Law and Economics, 1994, 16:25-71.

[40] Andreoni J. Reasonable doubt and the optimal magnitude of fines:Should the penalty fit the crime? The RAND Journal of Economics, 1991, 1(1):385-395.

[41] Saha A, Poole G. The economics of crime and punishment:An analysis of optimal penalty. Economics Letters, 2000, 68(2):191-196.

[42] Schweighofer E, Rauber A, Dittenbach M. Automatic text representation, classification and labeling in European law. In Proc. the 8th International Conference on Artificial Intelligence and Law, May 2001, pp.78-87.

[43] Feess E, Schramm M, Wohlschlegel A. The impact of fine size and uncertainty on punishment and deterrence:Theory and evidence from the laboratory. Journal of Economic Behavior & Organization, 2018, 149:58-73.
No related articles found!
Full text



[1] Zhou Di;. A Recovery Technique for Distributed Communicating Process Systems[J]. , 1986, 1(2): 34 -43 .
[2] Zhang Cui; Zhao Qinping; Xu Jiafu;. Kernel Language KLND[J]. , 1986, 1(3): 65 -79 .
[3] Zheng Guoliang; Li Hui;. The Design and Implementation of the Syntax-Directed Editor Generator(SEG)[J]. , 1986, 1(4): 39 -48 .
[4] Zhu Hong;. Some Mathematical Properties of the Functional Programming Language FP[J]. , 1987, 2(3): 202 -216 .
[5] Sun Yongqiang;. Verification of Systolic Array:An FP Functional Approach[J]. , 1988, 3(2): 81 -101 .
[6] Wang Hanhu;. Transaction Management in Distributed Database System POREL[J]. , 1988, 3(2): 139 -146 .
[7] Cai Zixing;. An Expert System for Robot Transfer Planning[J]. , 1988, 3(2): 153 -160 .
[8] Wang Nengbin; Liu Xiaoqing; Liu Guangfu;. A Software Tool for Constructing Traditional Chinese Medical Expert Systems[J]. , 1988, 3(3): 214 -220 .
[9] Wu Enhua;. An Implementation of the Viewing Pipeline in GKS-3D[J]. , 1988, 3(3): 228 -238 .
[10] Hong Jiarong; Carl Uhrik;. The ALFALFA Entomology Pest Identification System[J]. , 1988, 3(4): 251 -262 .

ISSN 1000-9000(Print)

CN 11-2296/TP

Editorial Board
Author Guidelines
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
E-mail: jcst@ict.ac.cn
  Copyright ©2015 JCST, All Rights Reserved