{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:22:33Z","timestamp":1762431753167,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322328"},{"type":"electronic","value":"9783030322335"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-32233-5_36","type":"book-chapter","created":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T22:04:51Z","timestamp":1569967491000},"page":"459-470","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Applying Data Discretization to DPCNN for Law Article Prediction"],"prefix":"10.1007","author":[{"given":"Hu","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongye","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ru","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,30]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Johnson, R., Zhang, T.: Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 562\u2013570 (2017)","DOI":"10.18653\/v1\/P17-1052"},{"issue":"1","key":"36_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2307\/1951767","volume":"51","author":"F Kort","year":"1957","unstructured":"Kort, F.: Predicting Supreme Court decisions mathematically: a quantitative analysis of the \u201cright to counsel\u201d cases. Am. Polit. Sci. Rev. 51(1), 1\u201312 (1957)","journal-title":"Am. Polit. Sci. Rev."},{"key":"36_CR3","doi-asserted-by":"publisher","first-page":"164","DOI":"10.2307\/1190728","volume":"28","author":"SS Ulmer","year":"1963","unstructured":"Ulmer, S.S.: Quantitative analysis of judicial processes: some practical and theoretical applications. Law Contemp. Probl. 28, 164 (1963)","journal-title":"Law Contemp. Probl."},{"key":"36_CR4","first-page":"1006","volume":"42","author":"SS Nagel","year":"1963","unstructured":"Nagel, S.S.: Applying correlation analysis to case prediction. Tex. L. Rev. 42, 1006 (1963)","journal-title":"Tex. L. Rev."},{"key":"36_CR5","first-page":"829","volume":"2","author":"R Keown","year":"1980","unstructured":"Keown, R.: Mathematical models for legal prediction. Comput. Law. J. 2, 829 (1980)","journal-title":"Comput. Law. J."},{"issue":"1","key":"36_CR6","first-page":"7","volume":"52","author":"EJ Ringquist","year":"1999","unstructured":"Ringquist, E.J., Emmert, C.E.: Judicial policymaking in published and unpublished decisions: the case of environmental civil ligaton. Polit. Res. Q. 52(1), 7\u201337 (1999)","journal-title":"Polit. Res. Q."},{"issue":"4","key":"36_CR7","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1017\/S0003055412000469","volume":"106","author":"BE Lauderdale","year":"2012","unstructured":"Lauderdale, B.E., Clark, T.S.: The Supreme Court\u2019s many median justices. Am. Polit. Sci. Rev. 106(4), 847\u2013866 (2012)","journal-title":"Am. Polit. Sci. Rev."},{"key":"36_CR8","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/11875604_75","volume-title":"International Symposium on Methodologies for Intelligent Systems","author":"CL Liu","year":"2006","unstructured":"Liu, C.L., Hsieh, C.D.: Exploring phrase-based classification of judicial documents for criminal charges in Chinese. In: Esposito, F., Ra\u015b, Z.W., Malerba, D., Semeraro, G. (eds.) International Symposium on Methodologies for Intelligent Systems, vol. 4203, pp. 681\u2013690. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11875604_75"},{"key":"36_CR9","unstructured":"Lin, W.C., Kuo, T.T., Chang, T.J., Yen, C.A., Chen, C.J., Lin, S.D.: Exploiting machine learning models for chinese legal documents labeling, case classification, and sentencing prediction. In: Proceedings of ROCLING, p. 140 (2012)"},{"key":"36_CR10","doi-asserted-by":"publisher","first-page":"e93","DOI":"10.7717\/peerj-cs.93","volume":"2","author":"N Aletras","year":"2016","unstructured":"Aletras, N., Tsarapatsanis, D., Preo\u0163iuc-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)","journal-title":"PeerJ. Comput. Sci."},{"key":"36_CR11","unstructured":"Sulea, O.M., Zampieri, M., Malmasi, S., Vela, M., Dinu, L.P., van Genabith, J.: Exploring the use of text classification in the legal domain. arXiv preprint arXiv:1710.09306 (2017)"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Katz, D.M., Bommarito, I.I., Michael, J., Blackman, J.: Predicting the behavior of the supreme court of the united states: a general approach. arXiv preprint arXiv:1407.6333 (2014)","DOI":"10.2139\/ssrn.2463244"},{"key":"36_CR13","first-page":"2493","volume":"12","author":"R Collobert","year":"2011","unstructured":"Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493\u20132537 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188 (2014)","DOI":"10.3115\/v1\/P14-1062"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Lei, T., Barzilay, R., Jaakkola, T.: Molding cnns for text: non-linear, non-consecutive convolutions. arXiv preprint arXiv:1508.04112 (2015)","DOI":"10.18653\/v1\/D15-1180"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, Z., Zhang, D., Yan, J.: Combining knowledge with deep convolutional neural networks for short text classification. In: IJCAI, pp. 2915\u20132921 (2017)","DOI":"10.24963\/ijcai.2017\/406"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Johnson, R., Zhang, T.: Effective use of word order for text categorization with convolutional neural networks. arXiv preprint arXiv:1412.1058 (2014)","DOI":"10.3115\/v1\/N15-1011"},{"key":"36_CR18","unstructured":"Zhang, X., Zhao, J., LeCun Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, pp. 649\u2013657 (2015)"},{"key":"36_CR19","unstructured":"Xiao, Y., Cho, K.: Efficient character-level document classification by combining convolution and recurrent layers. arXiv preprint arXiv:1602.00367 (2016)"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Luo, B., Feng, Y., Xu, J., Zhang, X., Zhao, D.: Learning to predict charges for criminal cases with legal basis. arXiv preprint arXiv:1707.09168 (2017)","DOI":"10.18653\/v1\/D17-1289"},{"key":"36_CR22","unstructured":"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, pp. 487\u2013498 (2018)"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Ye, H., Jiang, X., Luo, Z., Chao, W.: Interpretable charge predictions for criminal cases: learning to generate court views from fact descriptions. arXiv preprint arXiv:1802.08504 (2018)","DOI":"10.18653\/v1\/N18-1168"},{"issue":"1","key":"36_CR24","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.ipm.2014.07.003","volume":"51","author":"YH Liu","year":"2015","unstructured":"Liu, Y.H., Chen, Y.L., Ho, W.L.: Predicting associated statutes for legal problems. Inf. Process. Manage. 51(1), 194\u2013211 (2015)","journal-title":"Inf. Process. Manage."},{"key":"36_CR25","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-3-540-31849-1_8","volume-title":"Asia-Pacific Web Conference","author":"CL Liu","year":"2005","unstructured":"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.) Asia-Pacific Web Conference, pp. 64\u201375. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/978-3-540-31849-1_8"},{"key":"36_CR26","unstructured":"Tan, M., Santos, C.D., Xiang, B., Zhou, B.: LSTM-based deep learning models for non-factoid answer selection. arXiv preprint arXiv:1511.04108 (2015)"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32233-5_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:09:23Z","timestamp":1710353363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32233-5_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322328","9783030322335"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32233-5_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"30 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dunhuang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"softconf","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"492","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}