{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T02:39:31Z","timestamp":1783478371219,"version":"3.55.0"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Key Research and Development Program of Ningxia Hui Nationality Autonomous Region","award":["2022BEG02025"],"award-info":[{"award-number":["2022BEG02025"]}]},{"name":"Key Research and Development Program of Shaanxi Province","award":["2021GXLH-Z-095"],"award-info":[{"award-number":["2021GXLH-Z-095"]}]},{"name":"Industry Alignment Fund â Industry Collaboration Projects","award":["RIE2020"],"award-info":[{"award-number":["RIE2020"]}]},{"name":"Innovative Research Group of the National Natural Science Foundation of China","award":["61721002"],"award-info":[{"award-number":["61721002"]}]},{"name":"Innovation Research Team of the Ministry of Education"},{"name":"Project of China Knowledge Centre for Engineering Science and Technology","award":["IRT_17R86"],"award-info":[{"award-number":["IRT_17R86"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Affective Comput."],"published-print":{"date-parts":[[2023,7,1]]},"DOI":"10.1109\/taffc.2022.3202831","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T19:52:42Z","timestamp":1661889162000},"page":"1731-1742","source":"Crossref","is-referenced-by-count":58,"title":["Meta-Based Self-Training and Re-Weighting for Aspect-Based Sentiment Analysis"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2639-1532","authenticated-orcid":false,"given":"Kai","family":"He","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi’an Jiaotong University, Shanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1082-8755","authenticated-orcid":false,"given":"Rui","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3840-441X","authenticated-orcid":false,"given":"Tieliang","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi’an Jiaotong University, Shanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0079-3106","authenticated-orcid":false,"given":"Chen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi’an Jiaotong University, Shanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3030-1280","authenticated-orcid":false,"given":"Erik","family":"Cambria","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01071"},{"key":"ref57","article-title":"Self-paced learning for latent variable models","author":"kumar","year":"2010","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref12","article-title":"Uncertainty-aware self-training for text classification with few labels","author":"mukherjee","year":"2020"},{"key":"ref56","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"Proc 3rd Int Conf Learn Representations"},{"key":"ref15","first-page":"596","article-title":"Fix-Match: Simplifying semi-supervised learning with consistency and confidence","author":"sohn","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref14","article-title":"Mixmatch: A holistic approach to semi-supervised learning","author":"berthelot","year":"2019"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref53","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref52","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2019","journal-title":"Proc Conf North Amer Chapter Assoc Comput Linguistics Hum Lang Technol"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59722-1_42"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM52615.2021.9669450"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S15-2082"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1051"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-88483-3_18"},{"key":"ref16","first-page":"41","article-title":"Understanding deep learning requires rethinking generalization (2016)","author":"chiyuan","year":"2009","journal-title":"Proc 26th Annu Int Conf Mach Learn"},{"key":"ref19","article-title":"Semi-supervised relation extraction via incremental meta self-training","author":"hu","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00608"},{"key":"ref51","first-page":"315","article-title":"Deep sparse rectifier neural networks","author":"glorot","year":"2011","journal-title":"Proc 14th Int Conf Artif Intell Statist"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1466"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.620"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.565"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.234"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"ref41","first-page":"2304","article-title":"MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels","author":"jiang","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.484"},{"key":"ref43","first-page":"1189","article-title":"Self-paced learning for latent variable models","author":"kumar","year":"2010","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6447"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10974"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/583"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.340"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11923"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.06.011"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1048"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1051"},{"key":"ref40","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"Proc 33rd Int Conf Mach Learn"},{"key":"ref35","first-page":"1737","author":"wang","year":"2021","journal-title":"Meta Self-Training for Few-Shot Neural Sequence Labeling"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.426"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.3115\/1119176.1119183"},{"key":"ref36","first-page":"33","article-title":"Co-training and self-training for word sense disambiguation","author":"mihalcea","year":"2004","journal-title":"Proc 8th Conf Comput Natural Lang Learn"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.06.002"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1113"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.718"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1670"},{"key":"ref2","first-page":"11112","article-title":"HiTKG: Towards goal-oriented conversations via multi-hierarchy learning","author":"jinjie","year":"2022","journal-title":"Proc 36th AAAI Conf Artif Intell"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/S14-2004"},{"key":"ref39","first-page":"180:1","article-title":"A survey of deep active learning","volume":"54","author":"ren","year":"2021","journal-title":"ACM Comput Surv"},{"key":"ref38","first-page":"1002","article-title":"Active bias: Training more accurate neural networks by emphasizing high variance samples","author":"chang","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i15.17596"},{"key":"ref23","first-page":"2379","article-title":"Self-paced learning for long-term tracking","author":"supancic","year":"2013","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i15.17597"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1048"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1378"},{"key":"ref22","first-page":"1919","article-title":"Meta-weight-net: Learning an explicit mapping for sample weighting","author":"shu","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i10.21313"},{"key":"ref28","first-page":"6256","article-title":"Unsupervised data augmentation for consistency training","author":"xie","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87237-3_29"},{"key":"ref29","article-title":"Fine-tuning pre-trained language model with weak supervision: A contrastive-regularized self-training approach","author":"yu","year":"2020"}],"container-title":["IEEE Transactions on Affective Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5165369\/10254612\/09870538.pdf?arnumber=9870538","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T19:10:42Z","timestamp":1696878642000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9870538\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,1]]},"references-count":58,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/taffc.2022.3202831","relation":{},"ISSN":["1949-3045","2371-9850"],"issn-type":[{"value":"1949-3045","type":"electronic"},{"value":"2371-9850","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,1]]}}}