{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:50:13Z","timestamp":1781110213079,"version":"3.54.1"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100004884","name":"Research Innovation Team Fund from the Department of Education, Sichuan Province","doi-asserted-by":"publisher","award":["18TD0026"],"award-info":[{"award-number":["18TD0026"]}],"id":[{"id":"10.13039\/501100004884","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Science and Technology Program","doi-asserted-by":"publisher","award":["2020YFG0168"],"award-info":[{"award-number":["2020YFG0168"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3142178","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T20:37:24Z","timestamp":1641933444000},"page":"8518-8528","source":"Crossref","is-referenced-by-count":36,"title":["Unrestricted Attention May Not Be All You Need\u2013Masked Attention Mechanism Focuses Better on Relevant Parts in Aspect-Based Sentiment Analysis"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6231-7810","authenticated-orcid":false,"given":"Ao","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7927-7181","authenticated-orcid":false,"given":"Xuelei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9709-029X","authenticated-orcid":false,"given":"Xinyu","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014073"},{"key":"ref2","first-page":"452","article-title":"Opinion mining and sentiment analysis","volume-title":"Proc. 3rd Int. Conf. Comput. Sustain. Global Develop. (INDIACom)","author":"Bakshi"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2200\/S00416ED1V01Y201204HLT016"},{"key":"ref4","first-page":"151","article-title":"Target-dependent twitter sentiment classification","volume-title":"Proc. 49th Annu. Meeting Assoc. Comput. Linguistics, Hum. Lang. Technol.","author":"Jiang"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3098180"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.08.005"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/s14-2004"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S15-2082"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/s16-1002"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/S14-2149"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/s14-2076"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/S14-2036"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/S14-2038"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.13053\/cys-22-1-2784"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1058"},{"key":"ref16","article-title":"Effective LSTMs for target-dependent sentiment classification","volume-title":"arXiv:1512.01100","author":"Tang","year":"2015"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10380"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1088"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12049"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d17-1047"},{"key":"ref21","article-title":"Parameterized convolutional neural networks for aspect level sentiment classification","volume-title":"arXiv:1909.06276","author":"Huang","year":"2019"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref23","first-page":"4","article-title":"Automatic differentiation in PyTorch. NIPS-W","volume-title":"Proc. 31st Conf. Neural Inf. Process. Syst. (NIPS)","author":"Paszke"},{"key":"ref24","first-page":"5998","article-title":"Attention is all you need","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Vaswani"},{"key":"ref25","article-title":"Efficient estimation of word representations in vector space","volume-title":"arXiv:1301.3781","author":"Mikolov","year":"2013"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref27","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"arXiv:1810.04805","author":"Devlin","year":"2018"},{"key":"ref28","first-page":"5753","article-title":"XLNet: Generalized autoregressive pretraining for language understanding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Yang"},{"key":"ref29","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","volume-title":"arXiv:1907.11692","author":"Liu","year":"2019"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1021"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/568"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.07.047"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3390\/s19020234"},{"key":"ref34","article-title":"BiERU: Bidirectional emotional recurrent unit for conversational sentiment analysis","volume-title":"arXiv:2006.00492","author":"Li","year":"2020"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2021.105973"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1234"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.06.009"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"ref39","article-title":"Improving language understanding by generative pre-training","author":"Radford","year":"2018"},{"key":"ref40","article-title":"CAN: Constrained attention networks for multi-aspect sentiment analysis","volume-title":"arXiv:1812.10735","author":"Hu","year":"2018"},{"key":"ref41","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-30490-4_9","article-title":"Attentional encoder network for targeted sentiment classification","volume-title":"arXiv:1902.09314","author":"Song","year":"2019"},{"key":"ref42","article-title":"BERT post-training for review reading comprehension and aspect-based sentiment analysis","volume-title":"arXiv:1904.02232","author":"Xu","year":"2019"},{"key":"ref43","article-title":"Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence","volume-title":"arXiv:1903.09588","author":"Sun","year":"2019"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1654"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.293"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3049294"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2020.3017093"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107220"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2946594"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.486"},{"key":"ref51","article-title":"Adam: A method for stochastic optimization","volume-title":"arXiv:1412.6980","author":"Kingma","year":"2014"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n16-1174"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-5505"},{"key":"ref54","article-title":"Neural machine translation by jointly learning to align and translate","volume-title":"arXiv:1409.0473","author":"Bahdanau","year":"2014"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3560815"},{"key":"ref56","article-title":"Attention is not all you need: Pure attention loses rank doubly exponentially with depth","volume-title":"arXiv:2103.03404","author":"Dong","year":"2021"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09676694.pdf?arnumber=9676694","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T22:20:01Z","timestamp":1705184401000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9676694\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3142178","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}