{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T14:46:40Z","timestamp":1770562000316,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["108-2221-E-305-005 and 109-2221-E-305-014"],"award-info":[{"award-number":["108-2221-E-305-005 and 109-2221-E-305-014"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006705","name":"National Taipei University","doi-asserted-by":"crossref","award":["109E19103"],"award-info":[{"award-number":["109E19103"]}],"id":[{"id":"10.13039\/501100006705","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s10489-021-02516-x","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T06:02:47Z","timestamp":1624514567000},"page":"2884-2902","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A few-shot transfer learning approach using text-label embedding with legal attributes for law article prediction"],"prefix":"10.1007","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2784-9616","authenticated-orcid":false,"given":"Yuh-Shyan","family":"Chen","sequence":"first","affiliation":[]},{"given":"Shin-Wei","family":"Chiang","sequence":"additional","affiliation":[]},{"given":"Meng-Luen","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"key":"2516_CR1","doi-asserted-by":"crossref","unstructured":"Little RJA, Rubin DB(2019) Statistical Analysis with Missing Data, Third Edition. John Wiley & Sons","DOI":"10.1002\/9781119482260"},{"issue":"7","key":"2516_CR2","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1002\/cem.2618","volume":"28","author":"EH Puwakkatiya-Kankanamage","year":"2014","unstructured":"Puwakkatiya-Kankanamage EH, Garc\u00eda-Mu\u00f1oz S, Biegler LT (2014) An optimization-based undeflated PLS (OUPLS) method to handle missing data in the training set. J Chemom 28(7):575\u2013584","journal-title":"J Chemom"},{"key":"2516_CR3","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.chemolab.2015.05.006","volume":"146","author":"A Folch-Fortuny","year":"2015","unstructured":"Folch-Fortuny A, Arteaga F, Ferrer A (2015) PCA model building with missing data: new proposals and a comparative study. Chemom Intell Lab Syst 146:77\u201388","journal-title":"Chemom Intell Lab Syst"},{"key":"2516_CR4","unstructured":"Longadge R and Dongre S (2013) Class imbalance problem in data mining: review. arXiv:1305.1707 [cs.LG]"},{"issue":"Apr","key":"2516_CR5","first-page":"761","volume":"8","author":"AB Owen","year":"2007","unstructured":"Owen AB (2007) Infinitely imbalanced logistic regression. J Mach Learn Res 8(Apr):761\u2013773","journal-title":"J Mach Learn Res"},{"key":"2516_CR6","unstructured":"Zhu et al (2012) Class imbalance robust incremental LPSVM for data streams learning. Proc of the International Joint Conference on Neural Networks 1\u20138"},{"issue":"2013","key":"2516_CR7","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.neunet.2013.02.007","volume":"44","author":"Pang","year":"2013","unstructured":"Pang et al (2013) Dynamic class imbalance learning for incremental LPSVM. Neural Netw 44(2013):87\u2013100","journal-title":"Neural Netw"},{"key":"2516_CR8","doi-asserted-by":"crossref","unstructured":"Li K, Zhou G, Zhai J, Li F, Shao M(2019) Improved PSO_AdaBoost ensemble algorithm for imbalanced data. Sensors 19(6):1476","DOI":"10.3390\/s19061476"},{"issue":"1","key":"2516_CR9","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):9","journal-title":"J Big Data"},{"key":"2516_CR10","doi-asserted-by":"crossref","unstructured":"Tan et al (2018) A survey on deep transfer learning. Proc of the International Conference on Artificial Neural Networks 270\u2013279","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"2516_CR11","doi-asserted-by":"crossref","unstructured":"San et al (2019) Meta-transfer learning for few-shot learning. Proc of the IEEE Conference on Computer Vision and Pattern Recognition 403\u2013412","DOI":"10.1109\/CVPR.2019.00049"},{"key":"2516_CR12","unstructured":"Mikolov et al (2013) Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]"},{"key":"2516_CR13","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, and Dean J (2013) Distributed representations of words and phrases and their compositionality. Proc of the International Conference on Advances in Neural Information Processing Systems 3111-3119"},{"key":"2516_CR14","unstructured":"Miller EG, Matsakis NE and Viola PA (2000) Learning from one example through shared densities on transforms. Proc of the IEEE Conference on Computer Vision and Pattern Recognition 464\u2013471"},{"key":"2516_CR15","unstructured":"Lake et al (2011) One shot learning of simple visual concepts. Proc of the Annual Meeting of the Cognitive Science Society 2568\u20132573"},{"key":"2516_CR16","unstructured":"Jake S, Kevin S, and Richard SZ (2017) Prototypical networks for few-shot learning. arXiv:1703.05175 [cs.LG]"},{"key":"2516_CR17","unstructured":"Koch G (2015) Siamese neural networks for one-shot image recognition. Master\u2019s thesis, University of Toronto"},{"key":"2516_CR18","doi-asserted-by":"crossref","unstructured":"Liu CL et al (2005) Classifying criminal charges in Chinese for web-based legal services. Proc of the Asia-pacific Web Conference 64-75","DOI":"10.1007\/978-3-540-31849-1_8"},{"key":"2516_CR19","unstructured":"Lin SD et al (2012) Exploiting machine learning models for Chinese legal documents labeling, case classification, and sentencing prediction. Proc of ROCLING 49\u201368"},{"key":"2516_CR20","doi-asserted-by":"publisher","unstructured":"Luo B et al (2017) Learning to predict charges on a legal basis. arXiv:1707.09168 [cs.CL]. https:\/\/doi.org\/10.18653\/v1\/D17-1289","DOI":"10.18653\/v1\/D17-1289"},{"key":"2516_CR21","doi-asserted-by":"crossref","unstructured":"He C et al (2019) SECaps: a sequence enhanced capsule model for charge prediction. Proc of the International Conference on Artificial Neural Networks 227-239","DOI":"10.1007\/978-3-030-30490-4_19"},{"key":"2516_CR22","doi-asserted-by":"crossref","unstructured":"Ye H, Jiang X, Luo Z, and Chao W (2018) interpretable charge predictions for criminal cases: learning to generate court views from fact descriptions. Proc of International Conference on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 1854-1864","DOI":"10.18653\/v1\/N18-1168"},{"key":"2516_CR23","unstructured":"Feng Y et al (2019) Improving statue prediction via mining correlations. Proc of the Asian Conference on Machine Learning 710\u2013725"},{"key":"2516_CR24","doi-asserted-by":"crossref","unstructured":"Bao Q et al (2019) Charge prediction with legal attention. Proc of the International Conference on International Conference on Natural Language Processing and Chinese Computing 447-458","DOI":"10.1007\/978-3-030-32233-5_35"},{"key":"2516_CR25","unstructured":"Sutskever I, Vinyals O, and Le QV (2014) Sequence to sequence learning with neural networks. arXiv:1409.3215 [cs.CL]"},{"key":"2516_CR26","doi-asserted-by":"crossref","unstructured":"Shaw P, Uszkoreit J, and Vaswani A (2018) Self-attention with relative position representations. arXiv:1803.02155 [cs.CL]","DOI":"10.18653\/v1\/N18-2074"},{"key":"2516_CR27","unstructured":"Kang et al (2019) Creating auxiliary representations from charge definitions for criminal charge prediction. arXiv:1911.05202v1 [cs.CL]"},{"key":"2516_CR28","unstructured":"Hu Z, Li X, Tu C, Liu Z, and Sun M. (2018) Few-shot charge prediction with discriminative legal attributes. Proc of the International Conference on Computational Linguistics 487-498"},{"key":"2516_CR29","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"Chawla","year":"2002","unstructured":"Chawla et al (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"2516_CR30","doi-asserted-by":"crossref","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","DOI":"10.1145\/3065386"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02516-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02516-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02516-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T05:19:25Z","timestamp":1644470365000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02516-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,24]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["2516"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02516-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,24]]},"assertion":[{"value":"7 May 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}