{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T12:47:32Z","timestamp":1744894052802,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031171192"},{"type":"electronic","value":"9783031171208"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-17120-8_54","type":"book-chapter","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T13:02:58Z","timestamp":1663938178000},"page":"694-705","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Joint Label-Enhanced Representation Based on Pre-trained Model for Charge Prediction"],"prefix":"10.1007","author":[{"given":"Jingpei","family":"Dan","sequence":"first","affiliation":[]},{"given":"Xiaoshuang","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Lanlin","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Weixuan","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Tianyuan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"issue":"7","key":"54_CR1","first-page":"1006","volume":"42","author":"SS Nagel","year":"1964","unstructured":"Nagel, S.S.: Applying correlation analysis to case prediction. Tex. l. Rev. 42(7), 1006\u20131017 (1964)","journal-title":"Tex. l. Rev."},{"issue":"4","key":"54_CR2","doi-asserted-by":"publisher","first-page":"891","DOI":"10.2307\/1955796","volume":"78","author":"JA Segal","year":"1984","unstructured":"Segal, J.A.: Predicting supreme court cases probabilistically: the search and seizure cases, 1962\u20131981. Am. Political Sci. Rev. 78(4), 891\u2013900 (1984)","journal-title":"Am. Political Sci. Rev."},{"issue":"4","key":"54_CR3","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. Political Sci. Rev. 106(4), 847\u2013866 (2012)","journal-title":"Am. Political Sci. Rev."},{"issue":"4","key":"54_CR4","doi-asserted-by":"publisher","first-page":"e0174698","DOI":"10.1371\/journal.pone.0174698","volume":"12","author":"DM Katz","year":"2017","unstructured":"Katz, D.M., Bommarito, M.J., Blackman, J.: A general approach for predicting the behavior of the supreme court of the United States. PLoS ONE 12(4), e0174698 (2017)","journal-title":"PLoS ONE"},{"key":"54_CR5","unstructured":"Hu, Z., Li, X., Tu, C., Liu, Z., Sun., M.: Few-shot charge prediction with discriminative legal attributes. In: Proceedings of the COLING (2018)"},{"key":"54_CR6","doi-asserted-by":"crossref","unstructured":"Zhong, H., Guo, Z., Tu, C.: Legal judgment prediction via topological learning. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3540\u20133549. Association for Computational Linguistics, Brussels, Belgium (2018)","DOI":"10.18653\/v1\/D18-1390"},{"key":"54_CR7","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, pp. 2727\u20132736 (2017)","DOI":"10.18653\/v1\/D17-1289"},{"key":"54_CR8","doi-asserted-by":"crossref","unstructured":"Shaghaghian, S., Feng, L.Y., Jafarpour, B., Pogrebnyakov, N.: Customizing contextualized language models for legal document reviews. In: Proceedings of the IEEE International Conference on Big Data (Big Data), pp. 2139\u20132148 (2020)","DOI":"10.1109\/BigData50022.2020.9378201"},{"key":"54_CR9","doi-asserted-by":"crossref","unstructured":"Shao, Y., et al.: BERT-PLI: modeling paragraph-level interactions for legal case retrieval. In: Proceedings of IJCAI, pp. 3501\u20133507 (2020)","DOI":"10.24963\/ijcai.2020\/484"},{"key":"54_CR10","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. ArXiv abs\/1907.11692 (2019)"},{"key":"54_CR11","unstructured":"Zhong, H., Zhang, Z., Liu, Z., Sun, M.: Open Chinese Language Pre-trained Model Zoo. Technical Report (2019)"},{"key":"54_CR12","unstructured":"Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., Androutsopoulos, I.: LEGAL-BERT: \u201cpreparing the muppets for court\u201d. In: Proceedings of EMNLP: Findings, pp. 2898\u20132904 (2020)"},{"key":"54_CR13","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL HLT, pp. 4171\u20134186 (2019)"},{"key":"54_CR14","unstructured":"Xiao, C., et al.: CAIL2018: a large-scale legal dataset for judgment prediction. ArXiv abs\/1807.02478 (2018)"},{"key":"54_CR15","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. EMNLP (2014)","DOI":"10.3115\/v1\/D14-1181"},{"issue":"5","key":"54_CR16","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1016\/0306-4573(88)90021-0","volume":"24","author":"G Salton","year":"1988","unstructured":"Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513\u2013523 (1988)","journal-title":"Inf. Process. Manage."},{"issue":"3","key":"54_CR17","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293\u2013300 (1999)","journal-title":"Neural Process. Lett."},{"key":"54_CR18","unstructured":"Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 2048\u20132057 (2015)"},{"key":"54_CR19","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111\u20133119 (2013)"},{"key":"54_CR20","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"}],"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\/978-3-031-17120-8_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T13:10:35Z","timestamp":1663938635000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17120-8_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031171192","9783031171208"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17120-8_54","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 September 2022","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":"Guilin","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2022\/","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":"327","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":"73","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":"0","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":"22% - 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":"1.5","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)"}}]}}