Abstract
In the era of big data, enormous growth of various legal data leads to a huge burden on law professionals, which lies in the contradiction between the increasing number of legal cases and the shortage of judicial resources. This issue enlightens us to explore the key technologies in the computer-aided criminal case process lines. In this paper, we investigate an analogy-based method of legal case inspection. We use the document vector generated by Doc2Vec (semantics-based case feature, SCF) and the feature defined by the case judgement model (model-based case feature, MCF) as two ways to find similar cases. The measurement methods of similarity between two cases and the deviation of case judgment are also defined. Experimental results on a real-world dataset shows the effectiveness of our method. The recall rate of irrational cases when using the MCF is higher than that when using the SCF.
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References
Liu, C., Chang, C., Ho, J.: Case instance generation and refinement for case-based criminal summary judgments in Chinese. J. Inf. Sci. Eng. 20(4), 783–800 (2004)
Liu, C.-L., Hsieh, C.-D.: Exploring phrase-based classification of judicial documents for criminal charges in Chinese. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 681–690. Springer, Heidelberg (2006). https://doi.org/10.1007/11875604_75
Sulea, O., Zampieri, M., Malmasi, S., Vela, M., Dinu, L.P., van Genabith, J.: Exploring the use of text classification in the legal domain. In: Proceedings of the Second Workshop on Automated Semantic Analysis of Information in Legal Texts (2017)
Sulea, O., Zampieri, M., Vela, M., van Genabith, J.: Predicting the law area and decisions of French Supreme Court cases. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2017), pp. 716–722 (2017)
Katz, D.M., Bommarito II, M.J., Blackman, J.: A general approach for predicting the behavior of the supreme court of the United States. PLoS ONE 12(4), e0174698 (2017)
Lin, W., Kuo, T., Chang, T., Yen, C., Chen, C., Lin, S.: Exploiting machine learning models for Chinese legal documents labeling, case classification, and sentencing prediction. Comput. Linguist. Chin. Lang. Process. 17(4), 49–68 (2012)
Liu, Y., Chen, Y.: A two-phase sentiment analysis approach for judgement prediction. J. Inf. Sci. 44(5), 594–607 (2018)
Aletras, N., Tsarapatsanis, D., Preotiuc-Pietro, D., Lampos, V.: Predicting judicial decisions of the European Court of Human Rights: a natural language processing perspective. PeerJ Comput. Sci. 2, e93 (2016)
Liu, C.-L., Liao, T.-M.: Classifying criminal charges in Chinese for web-based legal services. In: Zhang, Y., Tanaka, K., Yu, J.X., Wang, S., Li, M. (eds.) APWeb 2005. LNCS, vol. 3399, pp. 64–75. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31849-1_8
Liu, Y., Chen, Y., Ho, W.: Predicting associated statutes for legal problems. Inf. Process. Manage. 51(1), 194–211 (2015)
Meng, R., Rice, S., Wang, J., Sun, X.: A fusion steganographic algorithm based on faster R-CNN. CMC Comput. Mater. Continua 55(1), 1–16 (2018)
Wang, R., Shen, M., Li, Y., Gomes, S.: Multi-task joint sparse representation classification based on fisher discrimination dictionary learning. CMC Comput. Mater. Continua 57(1), 25–48 (2018)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), pp. 1724–1734 (2014)
Palau, R.M., Moens, M.: Argumentation mining: the detection, classification and structure of arguments in text. In: Proceedings of the 12th International Conference on Artificial Intelligence and Law, pp. 98–107 (2009)
Hachey, B., Grover, C.: Extractive summarization of legal texts. Artif. Intell. Law 14(4), 305–345 (2006)
Farzindar, A., Lapalme, G.: Legal text summarization by exploration of the thematic structures and argumentative roles. In: Proceedings of the Text Summarization Branches Out Workshop, pp. 27–38 (2004)
Galgani, F., Compton, P., Hoffmann, A.: Combining different summarization techniques for legal text. In: Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data, pp. 115–123 (2012)
Acknowledgment
This work is supported by the National Key Research and Development Program of China under grants 2018YFC0830902 and 2016QY03D0501, and the National Natural Science Foundation of China (NSFC) under grants 61723022 and 61601146.
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Li, S., Guo, B., Cai, Y., Ye, L., Zhang, H., Fang, B. (2019). Legal Case Inspection: An Analogy-Based Approach to Judgment Evaluation. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_13
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