Abstract
Law article prediction is a task of predicting the relevant laws and regulations involved in a case according to the description text of the case, and it has broad application prospects in improving judicial efficiency. In the existing research work, researchers often only consider a single case, employing the neural network method to extract features for prediction, which lack the mining of related and common element information between different data. In order to solve this problem, we propose a law article prediction method that integrates the characteristics of common elements. It can effectively utilize the co-occurrence information of the training data, fully mine the relevant common elements between cases, and fuse local features. Experiments show that our method performs well.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bao Q, Zan H, Gong P, Chen J, Xiao Y (2019) Charge prediction with legal attention. In: CCF International Conference on Natural Language Processing and Chinese Computing, pp 447–458. Springer
Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Faulkner ASR, Gulcehre C et al. (2021) Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261
Bruna J, Zaremba W, Szlam A, LeCun Y (2014) Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203
Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616–1637
Chen H, Cai D, Dai W, Dai Z, Ding Y (2019) Charge-based prison term prediction with deep gating network. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 6362–6367
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems 28
Gan L, Kuang K, Yang Y, Wu F (2021) Judgment prediction via injecting legal knowledge into neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp 12866–12874
Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp 1263–1272. PMLR
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Hu Z, Li X, Tu C, Liu Z, Sun M (2018) Few-shot charge prediction with discriminative legal attributes. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 487–498
Kenton JDM-WC, Toutanova LK (2019) Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp 4171–4186
Kim Y (2014) Convolutional neural networks for sentence classification. In: EMNLP
Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: ICLR (Poster)
Lauderdale BE, Clark TS (2012) The supreme court’s many median justices. Am Political Sci Rev 106(4):847–866
Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2980–2988
Lin W-C, Kuo T-T, Chang T-J, Yen C-A, Chen C-J, Lin S-d (2012) Exploiting machine learning models for chinese legal documents labeling, case classification, and sentencing prediction. Proc ROCLING 17(4):140
Liu C-L, Chang C-T, Ho J-H (2004) Case instance generation and refinement for case-based criminal summary judgments in chinese
Liu C-L, Hsieh C-D (2006) Exploring phrase-based classification of judicial documents for criminal charges in chinese. In: International Symposium on Methodologies for Intelligent Systems, pp 681–690. Springer
Liu P, Qiu X, Huang X (2016) Recurrent neural network for text classification with multi-task learning. In: IJCAI
Liu X, You X, Zhang X, Wu J, Lv P (2020) Tensor graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp 8409–8416
Luo B, Feng Y, Xu J, Zhang X, Zhao D (2017) Learning to predict charges for criminal cases with legal basis. In: EMNLP
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26
Nagel SS (1963) Applying correlation analysis to case prediction. Tex L Rev 42:1006
Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24(5):513–523
Sulea O-M, Zampieri M, Malmasi S, Vela M, Dinu LP, van Genabith J (2017) Exploring the use of text classification in the legal domain. Arxiv e-prints arXiv:1710.09306, 1710
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30
Xiao C, Zhong H, Guo Z, Tu C, Liu Z, Sun M, Feng Y, Han X, Hu Z, Wang H et al. (2018) Cail2018: A large-scale legal dataset for judgment prediction. arXiv preprint arXiv:1807.02478
Xu N, Wang P, Chen L, Pan L, Wang X, Zhao J (2020) Distinguish confusing law articles for legal judgment prediction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 3086–3095
Yang W, Jia W, Zhou X, Luo Y (2019) Legal judgment prediction via multi-perspective bi-feedback network. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 4085–4091
Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 1480–1489
Yan G, Li Y, Shen S, Zhang S, Liu J (2019) Law article prediction based on deep learning. In: 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp 281–284. IEEE
Yao L, Mao C, Luo Y (2019) Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp 7370–7377
Ye H, Jiang X, Luo Z, Chao W (2018) Interpretable charge predictions for criminal cases: Learning to generate court views from fact descriptions. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp 1854–1864
Yue L, Liu Q, Jin B, Wu H, Zhang K, An Y, Cheng M, Yin B, Wu D (2021) Neurjudge: A circumstance-aware neural framework for legal judgment prediction. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 973-982
Zhang Y, Yu X, Cui Z, Wu S, Wen Z, Wang L (2020) Every document owns its structure: Inductive text classification via graph neural networks. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 334–339
Zhong H, Guo Z, Tu C, Xiao C, Liu Z, Sun M (2018) Legal judgment prediction via topological learning. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 3540–3549
Zhong H, Xiao C, Tu C, Zhang T, Liu Z, Sun M (2020) How does nlp benefit legal system: A summary of legal artificial intelligence. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 5218–5230
Acknowledgements
This work is supported by the National Key R &D Program of China under Grant No.2020TYC0832400.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Hou, Y., Cheng, G., Zhang, Y. et al. Methods of incorporating common element characteristics for law article prediction. Artif Intell Law (2023). https://doi.org/10.1007/s10506-023-09359-6
Accepted:
Published:
DOI: https://doi.org/10.1007/s10506-023-09359-6