Abstract
The automatic charge prediction aims to predict the result of the judgment through fact descriptions in criminal cases, which is an important application of intelligent legal judgment system. Generally, this task can be formalized into a multi-label prediction task (i.e., we treat fact descriptions as inputs, and charges as labels). Most previous works on this task usually exploit informative features from fact descriptions for prediction while ignoring the charge space information (e.g., co-occurrence relation of charges or descriptions of charges). To better explore the charge space, in this paper, we propose to establish a Legal Graph Network (LGN for short) to solve this problem. Specifically, LGN fuses all the charge information (i.e., charge descriptions or correlations) into a unified legal graph. Based on the legal graph, four types of charge relations are designed to capture informative relations among charges. Then LGN embeds these relations to learn the robust charge representations. Finally both charge representations and fact representations are fed into an attention-based neural network for prediction. Experimental results on three datasets show that the model we proposed can significantly outperform state-of-the-art multi-label classification methods.
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References
Cao, P., Liu, X., Zhao, D., Zaiane, O.: Cost sensitive ranking support vector machine for multi-label data learning. In: Abraham, A., Haqiq, A., Alimi, A.M., Mezzour, G., Rokbani, N., Muda, A.K. (eds.) HIS 2016. AISC, vol. 552, pp. 244–255. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52941-7_25
Fu, T., Lee, W., Lei, Z.: HIN2Vec: explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, 06–10 November 2017, pp. 1797–1806 (2017). https://doi.org/10.1145/3132847.3132953
Fürnkranz, J., Hüllermeier, E., Loza Menc’ia, E., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73(2), 133–153 (2008). https://doi.org/10.1007/s10994-008-5064-8
Grave, E., Mikolov, T., Joulin, A., Bojanowski, P.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, Volume 2: Short Papers, Valencia, Spain, 3–7 April 2017, pp. 427–431 (2017). https://aclanthology.info/papers/E17-2068/e17-2068
Hu, Z., Li, X., Tu, C., Liu, Z., Sun, M.: Few-shot charge prediction with discriminative legal attributes. In: Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018, Santa Fe, New Mexico, USA, 20–26 August 2018, pp. 487–498 (2018). https://aclanthology.info/papers/C18-1041/c18-1041
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, A meeting of SIGDAT, a Special Interest Group of the ACL, Doha, Qatar, 25–29 October 2014, pp. 1746–1751 (2014). http://aclweb.org/anthology/D/D14/D14-1181.pdf
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015. Conference Track Proceedings (2015). http://arxiv.org/abs/1412.6980
Long, S., Tu, C., Liu, Z., Sun, M.: Automatic judgment prediction via legal reading comprehension. CoRR abs/1809.06537 (2018). http://arxiv.org/abs/1809.06537
Manochandar, S., Punniyamoorthy, M.: Scaling feature selection method for enhancing the classification performance of support vector machines in text mining. Comput. Ind. Eng. 124, 139–156 (2018). https://doi.org/10.1016/j.cie.2018.07.008
Sudharshan, P.J., Petitjean, C., Spanhol, F.A., de Oliveira, L.E.S., Heutte, L., Honeine, P.: Multiple instance learning for histopathological breast cancer image classification. Expert Syst. Appl. 117, 103–111 (2019). https://doi.org/10.1016/j.eswa.2018.09.049
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 co-located with the 16th International Conference on Artificial Intelligence and Law (ICAIL 2017), London, UK, 16 June 2017 (2017). http://ceur-ws.org/Vol-2143/paper5.pdf
Tanaka, E.A., Nozawa, S.R., Macedo, A.A., Baranauskas, J.A.: A multi-label approach using binary relevance and decision trees applied to functional genomics. J. Biomed. Inform. 54, 85–95 (2015). https://doi.org/10.1016/j.jbi.2014.12.011
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, 17–21 September 2015, pp. 1422–1432 (2015). http://aclweb.org/anthology/D/D15/D15-1167.pdf
Tsoumakas, G., Vlahavas, I.P.: Random k -labelsets: an ensemble method for multilabel classification. In: Proceeding of 8th European Conference on Machine Learning, ECML 2007, Warsaw, Poland, 17–21 September 2007, pp. 406–417 (2007). https://doi.org/10.1007/978-3-540-74958-5_38
Wang, P., Yang, Z., Niu, S., Zhang, Y., Zhang, L., Niu, S.: Modeling dynamic pairwise attention for crime classification over legal articles. In: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, 08–12 July 2018, pp. 485–494 (2018). https://doi.org/10.1145/3209978.3210057
Xiao, C., et al.: CAIL2018: a large-scale legal dataset for judgment prediction. CoRR abs/1807.02478 (2018). http://arxiv.org/abs/1807.02478
Yadav, C.S., Sharan, A.: A new LSA and entropy-based approach for automatic text document summarization. Int. J. Semantic Web Inf. Syst. 14(4), 1–32 (2018). https://doi.org/10.4018/IJSWIS.2018100101
Zhang, M., Li, Y., Liu, X., Geng, X.: Binary relevance for multi-label learning: an overview. Front. Comput. Sci. 12(2), 191–202 (2018). https://doi.org/10.1007/s11704-017-7031-7
Zhang, M., Zhou, Z.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007). https://doi.org/10.1016/j.patcog.2006.12.019
Zhao, H., Rai, P., Du, L., Buntine, W.L.: Bayesian multi-label learning with sparse features and labels, and label co-occurrences. In: International Conference on Artificial Intelligence and Statistics, AISTATS 2018, Playa Blanca, Lanzarote, Canary Islands, Spain, 9–11 April 2018, pp. 1943–1951 (2018). http://proceedings.mlr.press/v84/zhao18b.html
Zhong, H., Squicciarini, A.C., Miller, D.J., Caragea, C.: A group-based personalized model for image privacy classification and labeling. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19–25 August 2017, pp. 3952–3958 (2017). https://doi.org/10.24963/ijcai.2017/552
Zhong, H., Guo, Z., Tu, C., Xiao, C., Liu, Z., Sun, M.: Legal judgment prediction via topological learning. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31 October–4 November 2018, pp. 3540–3549 (2018). https://aclanthology.info/papers/D18-1390/d18-1390
Zhong, H., et al.: Overview of CAIL2018: legal judgment prediction competition. CoRR abs/1810.05851 (2018). http://arxiv.org/abs/1810.05851
Acknowledgment
This research work was supported by the National Natural Science Foundation of China under Grant No. 61802029, and the fundamental Research for the Central Universities under Grant No. 500419741. We would like to thank the anonymous reviewers for their valuable comments.
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Chen, S., Wang, P., Fang, W., Deng, X., Zhang, F. (2019). Learning to Predict Charges for Judgment with Legal Graph. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_20
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