Abstract
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.
This work is supported in part by the National Key Research and Development Program of China (2016YFC0800805), and the Fundamental Research Funds for the Central Universities (021714380013).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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). https://doi.org/10.1007/3-540-44801-2_15
Chowdhury, G.G.: Introduction to Modern Information Retrieval. Facet Publishing, London (2010)
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)
Čubranić, D.: Automatic bug triage using text categorization. In: SEKE (2004)
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)
Earnhart, D.: Enforcement of environmental protection laws under communism and democracy. J. Law Econ. 40(2), 377–402 (1997)
Guha, S., Rastogi, R., Shim, K.: ROCK: a robust clustering algorithm for categorical attributes. Inf. Syst. 25(5), 345–366 (2000)
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). https://doi.org/10.1007/978-3-540-39964-3_62
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)
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)
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)
Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: International Conference on Machine Learning, pp. 957–966 (2015)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
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)
Milani, B.A., Navimipour, N.J.: A systematic literature review of the data replication techniques in the cloud environments. Big Data Research (2017)
Mnih, A., Hinton, G.E.: A scalable hierarchical distributed language model. In: Advances in Neural Information Processing Systems, pp. 1081–1088 (2009)
P’ng, I.P.: Strategic behavior in suit, settlement, and trial. Bell J. Econ. 539–550 (1983)
Polinsky, A.M., Shavell, S.: Punitive damages: an economic analysis. Harv. Law Rev. 111, 869–962 (1998)
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)
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)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)
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)
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)
Virpioja, S.: BIRCH: balanced iterative reducing and clustering using hierarchies (2008)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
He, T., Lian, H., Qin, Z., Zou, Z., Luo, B. (2018). Word Embedding Based Document Similarity for the Inferring of Penalty. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-02934-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02933-3
Online ISBN: 978-3-030-02934-0
eBook Packages: Computer ScienceComputer Science (R0)