Abstract
Nowadays, the judicial system has been hard to satisfy the growing judicial needs of the people. Therefore, the introduction of artificial intelligence into the judicial field is an inevitable trend. This paper incorporates deep learning into intelligent judicial sentencing and proposes a comprehensive network fusion model based on massive legal documents. The proposed method combines multiple networks, e.g., recurrent neural network and convolutional neural network, in the procedure of sentencing prediction. Specially, we use text classification and post-classification regression to predict the defendant’s conviction, articles of law related to the case and prison term. Moreover, we use the simulated gradient descent method to build a fusion model. Experimental results on legal documents datasets justify the effectiveness of the proposed method in sentencing prediction. The fused network model outperforms each individual model in terms of higher accuracy and stability when predicting the conviction, law article and prison term.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aletras, N., Tsarapatsanis, D., Preoţiuc-Pietro, D., Lampos, V.: Predicting judicial decisions of the European Court of Human Rights: a natural language processing perspective. PeerJ Comput. Sci. 24(2), e93 (2016). https://doi.org/10.7717/peerj-cs.93
Schild, U.J.: Criminal sentencing and intelligent decision support. In: Sartor, G., Branting, K. (eds.) Judicial Applications of Artificial Intelligence, pp. 47–98. Springer, Dordrecht (1998). https://doi.org/10.1007/978-94-015-9010-5_3
Zong, B.: On the application of artificial intelligence in the judgment of criminal proof standard. Sci. Law (J. Northwest Univ. Polit. Sci. Law). https://doi.org/10.16290/j.cnki.1674-5205.2019.01.004
Kantor, P.: Foundations of statistical natural language processing. Inf. Retrieval 4(1), 80–81 (2001). https://doi.org/10.1023/A:1011424425034
Mikolov, T., Sutskever, I., Chen, K., Dean, J., Corrado, G.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (2013)
Eszter, B., István, C., Dániel, K., et al.: Race, religion and the city: twitter word frequency patterns reveal dominant demographic dimensions in the United States. Social Science Electronic Publishing (2016). https://doi.org/10.1057/palcomms.2016.10
Ahmed, A., Siraj, M.Md., Anazida, Z.: Feature selection using information gain for improved structural-based alert correlation. Plos One 11(11) (2016). https://doi.org/10.1371/journal.pone.0166017
Sun, A., Lim, E., Liu, Y.: On strategies for imbalanced text classification using SVM: a comparative study. Decis. Support Syst. 48(1), 191–201 (2010). https://doi.org/10.1016/j.dss.2009.07.011
Arevian: Recurrent neural networks for robust real-world text classification. In: Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence (2007). https://doi.org/10.1109/wi.2007.126
Yoon, K.: Convolutional neural networks for sentence classification. Eprint arXiv (2014). https://doi.org/10.3115/v1/d14-1181
Armand, J., Edouard, G., Piotr, B., et al.: Bag of tricks for efficient text classification (2016). https://doi.org/10.18653/v1/e17-2068
Zhang, H., Xiao, L., Wang, Y., et al.: A generalized recurrent neural architecture for text classification with multi-task learning. In: Proceedings of the International Joint Conference on Artificial Intelligence (2017). https://doi.org/10.24963/ijcai.2017/473
Jagannatha, A., Yu, H.: Bidirectional RNN for medical event detection in electronic health records. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2016). https://doi.org/10.18653/v1/n16-1056
Rahul, D., Salemt, F.M.: Gate-variants of Gated Recurrent Unit (GRU) neural networks. In: Proceedings of the IEEE International Midwest Symposium on Circuits and Systems (2017). https://doi.org/10.1109/mwscas.2017.8053243
Chen, J., Li, D., Mirella, L.: Long Short-Term Memory-Networks for machine reading (2016). https://doi.org/10.18653/v1/d16-1053
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI) (2015)
Huang, C., Zhao, H.: Chinese Word segmentation: a decade review. J. Chin. Inf. Process. 21(3), 8–19 (2007). https://doi.org/10.3969/j.issn.1003-0077.2007.03.002
CAIL2018: A large-scale legal dataset for judgment prediction. arXiv preprint arXiv:1807.02478 (2018)
Jake, L., Martin, K., Naomi, A.: Points of significance: classification evaluation. Nat. Methods 13(8), 603–604 (2016). https://doi.org/10.1038/nmeth.3945
Yang, Y.: An evaluation of statistical approaches to MEDLINE indexing. In: Proceedings of the Conference of the American Medical Informatics Association (1996). https://doi.org/10.1023/a:1009982220290
Acknowledgment
This paper is supported by the National Key Research and Development Program of China (Grant No. 2018YFC1801605), the Fund of State Key Laboratory for Novel Software Technology at Nanjing University (No. ZZKT2018B01).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yin, Y., Yang, H., Zhao, Z., Chen, S. (2019). A Judicial Sentencing Method Based on Fused Deep Neural Networks. 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_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-30490-4_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30489-8
Online ISBN: 978-3-030-30490-4
eBook Packages: Computer ScienceComputer Science (R0)