Word Embedding Based Document Similarity for the Inferring of Penalty

  • Tieke HeEmail author
  • Hao Lian
  • Zemin Qin
  • Zhipeng Zou
  • Bin Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11242)


In this paper, we present a novel framework for the inferring of fine amount of judicial cases, which is based on word embedding when calculating the distances between documents. Our work is based on recent studies in word embeddings that learn semantically meaningful representations for words from local occurrences in sentences. This framework considers the context information of words by adopting the word2vec embedding, compared to traditional processing methods such as hierarchical clustering, kNN, k-means and traditional collaborative filtering that rely on vectors. In the area of judicial research, there exists the problem of deciding the amount of fine or penalty of legal cases, in this work we deal with it as a recommendation task, specifically, we divide all the legal cases into 7 classes by the amount of fine, and then for a target legal case, we try to infer which class this case belongs to. We conduct extensive experiments on a legal case dataset, and the results show that our proposed method outperforms all the comparative methods in metrics Precision, Recall and F1-Score.


Collaborative Filtering Word embedding Penalty inferring 


  1. 1.
    Boyer, M., Lewis, T.R., Liu, W.L.: Setting standards for credible compliance and law enforcement. Can. J. Econ./Rev. Can. D’économique 33(2), 319–340 (2000)Google Scholar
  2. 2.
    Chee, S.H.S., Han, J., Wang, K.: RecTree: an efficient collaborative filtering method. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 141–151. Springer, Heidelberg (2001). Scholar
  3. 3.
    Chowdhury, G.G.: Introduction to Modern Information Retrieval. Facet Publishing, London (2010)Google Scholar
  4. 4.
    Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)Google Scholar
  5. 5.
    Čubranić, D.: Automatic bug triage using text categorization. In: SEKE (2004)Google Scholar
  6. 6.
    Daughety, A.F., Reinganum, J.F.: Keeping society in the dark: on the admissibility of pretrial negotiations as evidence in court. RAND J. Econ. 203–221 (1995)CrossRefGoogle Scholar
  7. 7.
    Earnhart, D.: Enforcement of environmental protection laws under communism and democracy. J. Law Econ. 40(2), 377–402 (1997)CrossRefGoogle Scholar
  8. 8.
    Guha, S., Rastogi, R., Shim, K.: ROCK: a robust clustering algorithm for categorical attributes. Inf. Syst. 25(5), 345–366 (2000)CrossRefGoogle Scholar
  9. 9.
    Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) OTM 2003. LNCS, vol. 2888, pp. 986–996. Springer, Heidelberg (2003). Scholar
  10. 10.
    He, T., Chen, Z., Liu, J., Zhou, X., Du, X., Wang, W.: An empirical study on user-topic rating based collaborative filtering methods. World Wide Web 20(4), 815–829 (2017)CrossRefGoogle Scholar
  11. 11.
    He, T., Yin, H., Chen, Z., Zhou, X., Sadiq, S., Luo, B.: A spatial-temporal topic model for the semantic annotation of POIs in LBSNs. ACM Trans. Intell. Syst. Technol. (TIST) 8(1), 12 (2016)Google Scholar
  12. 12.
    Kilgour, D.M., Fang, L., Hipel, K.W.: Game-theoretic analyses of enforcement of environmental laws and regulations. JAWRA J. Am. Water Resour. Assoc. 28(1), 141–153 (1992)CrossRefGoogle Scholar
  13. 13.
    Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: International Conference on Machine Learning, pp. 957–966 (2015)Google Scholar
  14. 14.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  15. 15.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  16. 16.
    Milani, B.A., Navimipour, N.J.: A systematic literature review of the data replication techniques in the cloud environments. Big Data Research (2017)Google Scholar
  17. 17.
    Mnih, A., Hinton, G.E.: A scalable hierarchical distributed language model. In: Advances in Neural Information Processing Systems, pp. 1081–1088 (2009)Google Scholar
  18. 18.
    P’ng, I.P.: Strategic behavior in suit, settlement, and trial. Bell J. Econ. 539–550 (1983)CrossRefGoogle Scholar
  19. 19.
    Polinsky, A.M., Shavell, S.: Punitive damages: an economic analysis. Harv. Law Rev. 111, 869–962 (1998)CrossRefGoogle Scholar
  20. 20.
    Ramos, J., et al.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242, pp. 133–142 (2003)Google Scholar
  21. 21.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM (1994)Google Scholar
  22. 22.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)CrossRefGoogle Scholar
  23. 23.
    Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 259–266. ACM (2008)Google Scholar
  24. 24.
    Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384–394. Association for Computational Linguistics (2010)Google Scholar
  25. 25.
    Virpioja, S.: BIRCH: balanced iterative reducing and clustering using hierarchies (2008)Google Scholar
  26. 26.
    Wang, W., Chen, Z., Liu, J., Qi, Q., Zhao, Z.: User-based collaborative filtering on cross domain by tag transfer learning. In: Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining, pp. 10–17. ACM (2012)Google Scholar
  27. 27.
    Wilkin, G.A., Huang, X.: K-means clustering algorithms: implementation and comparison. In: 2007 Second International Multi-Symposiums on Computer and Computational Sciences. IMSCCS 2007, pp. 133–136. IEEE (2007)Google Scholar
  28. 28.
    Yin, H., Wang, W., Wang, H., Chen, L., Zhou, X.: Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Trans. Knowl. Data Eng. 29(11), 2537–2551 (2017)CrossRefGoogle Scholar
  29. 29.
    Zhou, L., Zhang, D.: NLPIR: a theoretical framework for applying natural language processing to information retrieval. J. Assoc. Inf. Sci. Technol. 54(2), 115–123 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tieke He
    • 1
    Email author
  • Hao Lian
    • 1
  • Zemin Qin
    • 1
  • Zhipeng Zou
    • 1
  • Bin Luo
    • 1
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

Personalised recommendations