Abstract
Under the background of judicial responsibility system, making similar judgments according to similar cases is vital for front-line judges to solve complicated problems such as non-standard use of law and inconsistency of judicial ruling standards. In this paper, a method is proposed for judicial cases based on the LDA topic model. The case, penalty and legal provisions were set. Gibbs Sampling algorithm was employed to estimate the probability distribution of topics on the implicit topic set in a text and calculate the similarity between texts by cosine similarity. The quality of screening was used as a final evaluation indicator. The verification of massive experiments shows that the case screening method based on LDA and cosine similarity has a satisfactory effect.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Aggarwal, C.C., Zhai, C.: Mining Text Data. Springer, New York (2012). https://doi.org/10.1007/978-1-4614-3223-4
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)
Brutlag, J.D., Meek, C.: Challenges of the email domain for text classification. In: ICML 2000, pp. 103–110 (2000)
Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_4
Gutierrez-Osuna, R., Nagle, H.T.: A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 29(5), 626–632 (1999)
Helfer, L.R.: The politics of judicial structure: creating the United States court of veterans appeals. Conn. L. Rev. 25, 155 (1992)
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, pp. 241–250. ACM (2000)
Kansheng, S., Jie, H., Liu, H.T., Zhang, N.T., Song, W.T.: Efficient text classification method based on improved term reduction and term weighting. J. China Univ. Posts Telecommun. 18, 131–135 (2011)
Le Cam, L., et al.: An approximation theorem for the poisson binomial distribution. Pac. J. Math. 10(4), 1181–1197 (1960)
Li, W., Sun, L., Zhang, D.K.: Text classification based on labeled-LDA model. Chin. J. Comput.-Chin. Ed.- 31(4), 620 (2008)
Lin, J., Wilbur, W.J.: Pubmed related articles: a probabilistic topic-based model for content similarity. BMC Bioinform. 8(1), 423 (2007)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 1, 76–80 (2003)
Manning, C.D., Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press (1999)
McKay, R.B.: The judiciary and nonjudicial activities. Law Contemp. Probl. 35(1), 9–36 (1970)
Méndez, J.R., Iglesias, E.L., Fdez-Riverola, F., Díaz, F., Corchado, J.M.: Tokenising, stemming and stopword removal on anti-spam filtering domain. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds.) CAEPIA 2005. LNCS (LNAI), vol. 4177, pp. 449–458. Springer, Heidelberg (2006). https://doi.org/10.1007/11881216_47
Mitchell, T., Buchanan, B., de Jong, G., Dietterich, T., Rosenbloon, P.: Machine learning annual review of computer science. J. Comput. Sci 4, 417–433 (1990)
Newman, D., Smyth, P., Welling, M., Asuncion, A.U.: Distributed inference for latent Dirichlet allocation. In: Advances in Neural Information Processing Systems, pp. 1081–1088 (2008)
Pizarro, D.: Nothing more than feelings? The role of emotions in moral judgment. J. Theory Soc. Behav. 30(4), 355–375 (2000)
Porteous, I., Newman, D., Ihler, A., Asuncion, A., Smyth, P., Welling, M.: Fast collapsed Gibbs sampling for latent Dirichlet allocation. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 569–577. ACM (2008)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J., et al.: Item-based collaborative filtering recommendation algorithms. Www 1, 285–295 (2001)
Si, X., Sun, M.: Tag-LDA for scalable real-time tag recommendation. J. Inf. Comput. Sci. 6(2), 1009–1016 (2009)
Small, M.L.: How many cases do I need?’ On science and the logic of case selection in field-based research. Ethnography 10(1), 5–38 (2009)
Soucy, P., Mineau, G.W.: A simple KNN algorithm for text categorization. In: Proceedings 2001 IEEE International Conference on Data Mining, pp. 647–648. IEEE (2001)
Steyvers, M., Griffiths, T.: Probabilistic topic models. Handb. Latent Semant. Anal. 427(7), 424–440 (2007)
Sun, Q., Li, R., Luo, D., Wu, X.: Text segmentation with LDA-based Fisher kernel. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers, pp. 269–272. Association for Computational Linguistics (2008)
Wei, X., Croft, W.B.: LDA-based document models for ad-hoc retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 178–185. ACM (2006)
Weng, S.S., Tsai, H.J., Liu, S.C., Hsu, C.H.: Ontology construction for information classification. Expert Syst. Appl. 31(1), 1–12 (2006)
Zelikovitz, S., Hirsh, H.: Using LSI for text classification in the presence of background text. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, pp. 113–118. ACM (2001)
Zhang, W., Yoshida, T., Tang, X.: A comparative study of TF* IDF, LSI and multi-words for text classification. Expert Syst. Appl. 38(3), 2758–2765 (2011)
Acknowledgement
The work is supported in part by the National Key Research and Development Program of China (2016YFC0800805) and the National Natural Science Foundation of China (61772014).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xu, J., He, T., Lian, H., Wan, J., Kong, Q. (2019). Judicial Case Screening Based on LDA. In: Cheng, X., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1058. Springer, Singapore. https://doi.org/10.1007/978-981-15-0118-0_39
Download citation
DOI: https://doi.org/10.1007/978-981-15-0118-0_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0117-3
Online ISBN: 978-981-15-0118-0
eBook Packages: Computer ScienceComputer Science (R0)