Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 756–767 | Cite as

PTM: A Topic Model for the Inferring of the Penalty

  • Tie-Ke HeEmail author
  • Hao Lian
  • Ze-Min Qin
  • Zhen-Yu Chen
  • Bin Luo
Regular Paper


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.


penalty inferring topic model convolutional neural network support vector machine 


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  1. [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.CrossRefGoogle Scholar
  2. [2]
    Becker G S. Crime and punishment: An economic approach. The Journal Political Ewnomy, 1968, 76(2): 169-217.CrossRefGoogle Scholar
  3. [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.CrossRefGoogle Scholar
  4. [4]
    P’ng I P. Strategic behavior in suit, settlement, and trial. The Bell Journal of Economics, 1983, 14(2): 539-550.CrossRefGoogle Scholar
  5. [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.CrossRefGoogle Scholar
  6. [6]
    Polinsky A M, Shavell S. Punitive damages: An economic analysis. Harvard Law Review, 1998, 111(4): 869-962.CrossRefGoogle Scholar
  7. [7]
    Earnhart D. Enforcement of environmental protection laws under communism and democracy. The Journal of Law and Economics, 1997, 40(2): 377-402.CrossRefGoogle Scholar
  8. [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.Google Scholar
  9. [9]
    Yuan Q, Cong G, Ma Z, Sun A, Thalmann N M. Time-aware point-of-interest recommendation. In Proc. the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2013, pp.363-372.Google Scholar
  10. [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.Google Scholar
  11. [11]
    Wallach H M, Mimno D M, McCallum A. Rethinking LDA: Why priors matter. Advances in Neural Information Processing Systems, 2009, 23: 1973-1981.Google Scholar
  12. [12]
    Yin H, Sun Y, Cui B, Hu Z, Chen L. LCARS: A location-content-aware recommender system. In Proc. the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2013, pp.221-229.Google Scholar
  13. [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.Google Scholar
  14. [14]
    Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3): 273-297.zbMATHGoogle Scholar
  15. [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.Google Scholar
  16. [16]
    Kim Y. Convolutional neural networks for sentence classification. arXiv:1408.5882. 2014., April 2018.
  17. [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.CrossRefGoogle Scholar
  18. [18]
    Linden G, Smith B, York J. recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 2003, 7(1): 76-80.CrossRefGoogle Scholar
  19. [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.Google Scholar
  20. [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.Google Scholar
  21. [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.Google Scholar
  22. [22]
    Chowdhury G. Introduction to Modern Information Retrieval (3rd edition). Facet Publishing, 2010.Google Scholar
  23. [23]
    Su X, Khoshgoftaar TM, Greiner R. A mixture imputation-boosted collaborative filter. In Proc. the 21st International Florida Artificial Intelligence Research Socitey Conference, May 2008, pp.312-316.Google Scholar
  24. [24]
    Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003, 3(Jan): 993-1022.Google Scholar
  25. [25]
    Hofmann T. Probabilistic latent semantic analysis. In Proc. the 15th Conference on Uncertainty in Artificial Intelligence, July 1999, pp.289-296.Google Scholar
  26. [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.Google Scholar
  27. [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.Google Scholar
  28. [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.Google Scholar
  29. [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.Google Scholar
  30. [30]
    Yin H, Cui B, Huang Z, Wang W, Wu X, Zhou X. Joint modeling of users’ interests and mobility patterns for point-of-interest recommendation. In Proc. the 23rd ACM International Conference on Multimedia, October 2015, pp.819-822.Google Scholar
  31. [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.Google Scholar
  32. [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.Google Scholar
  33. [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.Google Scholar
  34. [34]
    Wang W, Yin H, Chen L, Sun Y, Sadiq S, Zhou X. Geo-SAGE: 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.Google Scholar
  35. [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.Google Scholar
  36. [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.Google Scholar
  37. [37]
    Yin H, Cui B, Chen L, Hu Z, Huang Z. A temporal context-aware 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.Google Scholar
  38. [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.Google Scholar
  39. [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.Google Scholar
  40. [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.CrossRefGoogle Scholar
  41. [41]
    Saha A, Poole G. The economics of crime and punishment: An analysis of optimal penalty. Economics Letters, 2000, 68(2): 191-196.CrossRefzbMATHGoogle Scholar
  42. [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.Google Scholar
  43. [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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Tie-Ke He
    • 1
    • 2
    Email author
  • Hao Lian
    • 1
    • 2
  • Ze-Min Qin
    • 1
    • 2
  • Zhen-Yu Chen
    • 1
    • 2
  • Bin Luo
    • 1
    • 2
  1. 1.Software InstituteNanjing UniversityNanjingChina
  2. 2.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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