{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:25:11Z","timestamp":1740122711512,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s10489-022-03675-1","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T07:02:21Z","timestamp":1651042941000},"page":"1306-1323","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Interpretable prison term prediction with reinforce learning and attention"],"prefix":"10.1007","volume":"53","author":[{"given":"Peipeng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0204-0295","authenticated-orcid":false,"given":"Xiuguo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiying","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"issue":"2","key":"3675_CR1","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s10506-019-09255-y","volume":"28","author":"M Medvedeva","year":"2020","unstructured":"Medvedeva M, Vols M, Wieling M (2020) Using machine learning to predict decisions of the European court of human rights[J]. Artif Intell Law 28(2):237\u2013266","journal-title":"Artif Intell Law"},{"key":"3675_CR2","first-page":"213","volume":"55","author":"Z Xiong","year":"2018","unstructured":"Xiong Z, Shen Q, Wang Y (2018) Paragraph vector representation based on word to vector and CNN learning[J]. CMC-Comput Mater Contin 55:213\u2013227","journal-title":"CMC-Comput Mater Contin"},{"key":"3675_CR3","doi-asserted-by":"publisher","first-page":"17821","DOI":"10.1007\/s00500-020-05029-w","volume":"24","author":"H Dong","year":"2020","unstructured":"Dong H, Yang F, Wang X (2020) Multi-label charge predictions leveraging label co-occurrence in imbalanced data scenario[J]. Soft Comput 24:17821\u201317846","journal-title":"Soft Comput"},{"issue":"4","key":"3675_CR4","doi-asserted-by":"publisher","first-page":"2233","DOI":"10.1007\/s10489-020-01912-z","volume":"51","author":"XD Guo","year":"2021","unstructured":"Guo XD, Zhang HL, Ye L, Li S (2021) TenLa: an approach based on controllable tensor decomposition and optimized lasso regression for judgement prediction of legal cases[J]. Appl Intell 51(4):2233\u20132252","journal-title":"Appl Intell"},{"key":"3675_CR5","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1613\/jair.1.11377","volume":"66","author":"WH Chao","year":"2019","unstructured":"Chao WH, Jiang X, Luo ZC (2019) Interpretable charge prediction for criminal cases with dynamic rationale attention[J]. J Artif Intell Res 66:743\u2013764","journal-title":"J Artif Intell Res"},{"key":"3675_CR6","doi-asserted-by":"publisher","first-page":"101569","DOI":"10.1109\/ACCESS.2020.2998108","volume":"8","author":"XC Li","year":"2020","unstructured":"Li XC, Kang XJ, Wang CW et al (2020) A neural-network-based model of charge prediction via the judicial interpretation of crimes [J]. IEEE Access 8:101569\u2013101579","journal-title":"IEEE Access"},{"issue":"3","key":"3675_CR7","first-page":"1217","volume":"61","author":"S Li","year":"2019","unstructured":"Li S, Zhang H, Ye L et al (2019) Prison term prediction on criminal case description with deep learning[J]. Comput Mater Contin 61(3):1217\u20131231","journal-title":"Comput Mater Contin"},{"key":"3675_CR8","first-page":"1480","volume-title":"Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies. San Diego, California, USA, June 12\u201317, 2016","author":"Z Yang","year":"2016","unstructured":"Yang Z, Yang D, Dyer C et al (2016) Hierarchical attention networks for document classification[C]. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies. San Diego, California, USA, June 12\u201317, 2016, pp 1480\u20131489"},{"key":"3675_CR9","doi-asserted-by":"crossref","unstructured":"Xu N, Wang P, Chen L et al Distinguish confusing law articles for legal judgment prediction[C]. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, online, July 5\u201310, 2020, pp 3086\u20133095","DOI":"10.18653\/v1\/2020.acl-main.280"},{"key":"3675_CR10","doi-asserted-by":"crossref","unstructured":"Cheng X, Bi S, Qi G et al Knowledge-aware method for confusing charge prediction[C]. In: CCF International Conference on Natural Language Processing and Chinese Computing. Zhengzhou, China, October 14\u201318, 2020, pp 667\u2013679","DOI":"10.1007\/978-3-030-60450-9_53"},{"key":"3675_CR11","doi-asserted-by":"crossref","unstructured":"Zhong H, Guo ZP et al Legal judgment prediction via topological learning[C]. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium, November 4, 2018, pp 3540\u20133549","DOI":"10.18653\/v1\/D18-1390"},{"key":"3675_CR12","doi-asserted-by":"crossref","unstructured":"Yang WM, Jia WJ et al (2019) Legal judgment prediction via multi-perspective bi-feedback network[C]. International Joint Conference on Artificial Intelligence. Macao, China, August 10\u201316, 2019, pp 4085\u20134091","DOI":"10.24963\/ijcai.2019\/567"},{"key":"3675_CR13","doi-asserted-by":"crossref","unstructured":"Ye H, Jiang X, Luo Z et al Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions[C]. Proceedings of the 2018 Conference of the north American chapter of the Association for Computational Linguistics: human language technologies, New Orleans, Louisiana, USA, June 1-6, 2018, pp 1854\u20131864","DOI":"10.18653\/v1\/N18-1168"},{"key":"3675_CR14","doi-asserted-by":"crossref","unstructured":"Zhong H, Wang Y, Tu C et al Iteratively questioning and answering for interpretable legal judgment prediction[C]. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA, February 7\u201312, 2020, 34(01), pp 1250\u20131257","DOI":"10.1609\/aaai.v34i01.5479"},{"issue":"1","key":"3675_CR15","first-page":"384","volume":"25","author":"L Li","year":"2022","unstructured":"Li L, Zhao LY, Nai PR, Tao XH (2022) Charge prediction modeling with interpretation enhancement driven by double-layer criminal system[J]. World Wide Web-Internet AND Web Information Systems 25(1):384\u2013400","journal-title":"World Wide Web-Internet AND Web Information Systems"},{"key":"3675_CR16","doi-asserted-by":"crossref","unstructured":"Chen HJ, Cai D et al Charge-Based Prison Term Prediction with Deep Gating Network. [C] Proceedings of the 2019 Conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, pp 6361\u20136366","DOI":"10.18653\/v1\/D19-1667"},{"issue":"3","key":"3675_CR17","doi-asserted-by":"publisher","first-page":"2884","DOI":"10.1007\/s10489-021-02516-x","volume":"52","author":"YS Chen","year":"2022","unstructured":"Chen YS, Chiang SW, Wu ML (2022) A few-shot transfer learning approach using text-label embedding with legal attributes for law article prediction[J]. Appl Intell 52(3):2884\u20132902","journal-title":"Appl Intell"},{"issue":"1","key":"3675_CR18","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1109\/TDSC.2020.2974727","volume":"19","author":"D Ranathunga","year":"2022","unstructured":"Ranathunga D, Roughan M, Nguyen H (2022) Verifiable policy-defined networking using Metagraphs[J]. IEEE Trans Dependable Secure Comput 19(1):482\u2013494","journal-title":"IEEE Trans Dependable Secure Comput"},{"issue":"2","key":"3675_CR19","doi-asserted-by":"publisher","first-page":"210","DOI":"10.3390\/jmse9020210","volume":"9","author":"S Guo","year":"2021","unstructured":"Guo S, Zhang X, Du Y et al (2021) Path planning of coastal ships based on optimized DQN reward function[J]. J Mar Sci Eng 9(2):210\u2013233","journal-title":"J Mar Sci Eng"},{"key":"3675_CR20","first-page":"6053","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence. New Orleans, LA, USA, February 2\u20137","author":"T Zhang","year":"2018","unstructured":"Zhang T.; Huang M.; Zhao L. Learning structured representation for text classification via reinforcement learning[C]. Proceedings of the AAAI Conference on Artificial Intelligence. New Orleans, LA, USA, February 2\u20137, 2018, 32(1), pp: 6053\u20136060"},{"key":"3675_CR21","doi-asserted-by":"crossref","unstructured":"Liu Z, Di XQ, Song W (2021) A sentence-level joint relation classification model based on reinforcement learning [J]. Comput Intell Neurosci","DOI":"10.1155\/2021\/5557184"},{"issue":"5","key":"3675_CR22","first-page":"2015","volume":"33","author":"QN Zhu","year":"2021","unstructured":"Zhu QN, Zhou XF, Tan JL, Guo L (2021) Knowledge base reasoning with convolutional-based recurrent neural networks[J]. IEEE Trans Knowl Data Eng 33(5):2015\u20132028","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3675_CR23","unstructured":"Le ML, Yi DW et al (2022) Deep reinforcement learning in computer vision: a comprehensive survey[J]. IEEE Trans Intell Transp Syst"},{"issue":"8","key":"3675_CR24","doi-asserted-by":"publisher","first-page":"3429","DOI":"10.1109\/TAC.2020.3029317","volume":"66","author":"S Paternain","year":"2021","unstructured":"Paternain S, Bazerque JA, Small A (2021) Ribeiro. A. Stochastic policy gradient ascent in reproducing kernel Hilbert spaces[J]. IEEE Trans Autom Control 66(8):3429\u20133444","journal-title":"IEEE Trans Autom Control"},{"key":"3675_CR25","volume-title":"1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4","author":"T Mikolov","year":"2013","unstructured":"Mikolov T, Chen K, Corrado G et al (2013) Efficient estimation of word representations in vector space[C]. In: 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4"},{"key":"3675_CR26","first-page":"4171","volume-title":"Proceedings of the 2019 Conference of the north American chapter of the Association for Computational Linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7","author":"J Devlin","year":"2019","unstructured":"Devlin J et al (2019) Bert: Pre-training of deep bidirectional transformers for language understanding[C]. In: Proceedings of the 2019 Conference of the north American chapter of the Association for Computational Linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, pp 4171\u20134186"},{"issue":"1","key":"3675_CR27","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/TCSVT.2021.3067449","volume":"32","author":"CG Yan","year":"2022","unstructured":"Yan CG, Hao YM et al (2022) Task-adaptive attention for image captioning[J]. IEEE Trans Circuits Syst Video Technol 32(1):43\u201351","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"3675_CR28","doi-asserted-by":"publisher","first-page":"107238","DOI":"10.1016\/j.knosys.2021.107238","volume":"228","author":"A Mee","year":"2021","unstructured":"Mee A, Homapour E, Chiclana F, Engel O (2021) Sentiment analysis using TF-IDF weighting of UK MPs' tweets on Brexit[J]. Knowl-Based Syst 228:107238","journal-title":"Knowl-Based Syst"},{"key":"3675_CR29","first-page":"371","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy, June 28\u2013August 2","author":"HY Zied","year":"2019","unstructured":"Zied HY, Sieg A, Deleris LA (2019) Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings[C]. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy, June 28\u2013August 2, pp 371\u2013379"},{"issue":"2","key":"3675_CR30","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1007\/s11063-019-10003-1","volume":"52","author":"SN Xiao","year":"2020","unstructured":"Xiao SN, Li YM, Ye YA et al (2020) Hierarchical temporal fusion of multi-grained attention features for video question answering[J]. Neural Process Lett 52(2):993\u20131003","journal-title":"Neural Process Lett"},{"issue":"12","key":"3675_CR31","first-page":"56","volume":"32","author":"C Sun","year":"2018","unstructured":"Sun C, Kong F (2018) The awarding ceremony of \"China legal research cup\" judicial artificial intelligence challenge (Cail 2018) was held [J]. Chin J inf 32(12):56","journal-title":"Chin J inf"},{"key":"3675_CR32","unstructured":"Sun MS, Chen XX, Zhang KX et al (2016) Thulac: An efficient lexical analyzer for chinese. Technical Report"},{"key":"3675_CR33","volume-title":"3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9","author":"K Diederik","year":"2015","unstructured":"Diederik K, Jimmy B (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9"},{"issue":"1","key":"3675_CR34","first-page":"1929","volume":"15","author":"S Nitish","year":"2014","unstructured":"Nitish S, Geoffrey EH, Alex K, Ilya S, Ruslan S (2014) Dropout: a simple way to prevent neural networks from overfitting[J]. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"3675_CR35","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.ins.2020.08.090","volume":"544","author":"K Cheng","year":"2021","unstructured":"Cheng K, Lu ZZ (2021) Active learning Bayesian support vector regression model for global approximation[J]. Inf Sci 544:549\u2013563","journal-title":"Inf Sci"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03675-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03675-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03675-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T04:33:33Z","timestamp":1672806813000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03675-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,27]]},"references-count":35,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["3675"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03675-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,4,27]]},"assertion":[{"value":"21 April 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}