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Abstract
With the rapid development of artificial intelligence, it is a general trend to apply artificial intelligence to the judicial field. The use of deep learning to analyze judicial data can assist judicial decisions, and can also assist in legal counseling. Artificial intelligence can promote the intelligence and automation of the judiciary and improve the efficiency of the judicial process. The analysis of judicial texts has always been a research hotspot of judicial intelligence. Using deep learning to analyze judicial texts and screening similar cases according to the similarity of cases can provide objective and effective assistance for judicial decisions. This paper designs a method for analyzing case facts based on deep learning and recommending similar cases. The method implements word segmentation on the case facts of the training set cases and trains the word vector. Then, extractingk keywords of the ‘fact’ field for each training case. The representative central word vector of the case is constructed according to the keywords. The representative central word vector for each test case is constructed in the same way, and the training case to be recommended for the test case is the one which has the most similar vector to the vector of the test case. We used this method to implement the recommendation of judicial cases in experiment, and designed a verification experiment to evaluate the accuracy of the recommendation. The recommendation accuracy rate reached\(91.3\%\).
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References
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI, pp. 43–52 (1998)
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
Gray, P.N.: The ontology of legal possibilities and legal potentialities. In: LOAIT, pp. 7–23 (2007)
He, T., Lian, H., Qin, Z., Zou, Z., Luo, B.: Word embedding based document similarity for the inferring of penalty. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds.) WISA 2018. LNCS, vol. 11242, pp. 240–251. Springer, Cham (2018).https://doi.org/10.1007/978-3-030-02934-0_22
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: CSCW, pp. 241–250 (2000)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput.7(1), 76–80 (2003)
Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: EMNLP (2004)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprintarXiv:1301.3781 (2013)
Nanda, R., Adebayo, K.J., Di Caro, L., Boella, G., Robaldo, L.: Legal information retrieval using topic clustering and neural networks. In: COLIEE@ ICAIL, pp. 68–78 (2017)
Prakken, H.: Modelling reasoning about evidence in legal procedure. In: ICAIL, pp. 119–128 (2001)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J., et al.: Item-based collaborative filtering recommendation algorithms. In: WWW, vol. 1, pp. 285–295 (2001)
Xiao, C., et al.: Cail 2018: a large-scale legal dataset for judgment prediction. arXiv preprintarXiv:1807.02478 (2018)
Acknowledgment
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).
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State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China
Zihuan Xu, Tieke He, Hao Lian, Jiabing Wan & Hui Wang
- Zihuan Xu
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- Hao Lian
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- Jiabing Wan
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- Hui Wang
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Correspondence toTieke He.
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Southeast University, Nanjing, China
Weiwei Ni
Tianjin University, Tianjin, China
Xin Wang
Wuhan University, Wuhan, China
Wei Song
Tianjin University of Technology, Tianjin, China
Yukun Li
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Xu, Z., He, T., Lian, H., Wan, J., Wang, H. (2019). Case Facts Analysis Method Based on Deep Learning. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_11
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