{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T22:10:05Z","timestamp":1756246205236,"version":"3.44.0"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62206194"],"award-info":[{"award-number":["62206194"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20220488"],"award-info":[{"award-number":["BK20220488"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Research Grants Council","award":["16202722","T22-607\/24-N","T43-513\/23N-1"],"award-info":[{"award-number":["16202722","T22-607\/24-N","T43-513\/23N-1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1109\/tkde.2025.3567204","type":"journal-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T13:58:38Z","timestamp":1746453518000},"page":"4880-4893","source":"Crossref","is-referenced-by-count":1,"title":["Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5340-7256","authenticated-orcid":false,"given":"Yuyang","family":"Ding","sequence":"first","affiliation":[{"name":"Soochow University, Suzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9689-5308","authenticated-orcid":false,"given":"Dan","family":"Qiao","sequence":"additional","affiliation":[{"name":"Soochow University, Suzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6286-7529","authenticated-orcid":false,"given":"Juntao","family":"Li","sequence":"additional","affiliation":[{"name":"Soochow University, Suzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8227-8636","authenticated-orcid":false,"given":"Jiajie","family":"Xu","sequence":"additional","affiliation":[{"name":"Soochow University, Suzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4892-9041","authenticated-orcid":false,"given":"Pingfu","family":"Chao","sequence":"additional","affiliation":[{"name":"Soochow University, Suzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6343-1455","authenticated-orcid":false,"given":"Xiaofang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3895-5510","authenticated-orcid":false,"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"Soochow University, Suzhou, China"}]}],"member":"263","reference":[{"article-title":"A survey on recent advances in named entity recognition from deep learning models","year":"2019","author":"Yadav","key":"ref1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981314"},{"article-title":"Bidirectional LSTM-CRF models for sequence tagging","year":"2015","author":"Huang","key":"ref3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1283"},{"article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","year":"2018","author":"Devlin","key":"ref5"},{"article-title":"UniversalNER: Targeted distillation from large language models for open named entity recognition","year":"2023","author":"Zhou","key":"ref6"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3241741"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403149"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.626"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1230"},{"key":"ref11","first-page":"7260","article-title":"Dual T: Reducing estimation error for transition matrix in label-noise learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yao"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1231"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.498"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/11564096_24"},{"article-title":"Empirical analysis of unlabeled entity problem in named entity recognition","year":"2020","author":"Li","key":"ref15"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.497"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.300"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.131"},{"article-title":"An overview of distant supervision for relation extraction with a focus on denoising and pre-training methods","year":"2022","author":"Hogan","key":"ref19"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1397"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/PRML52754.2021.9520707"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3252084"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.490"},{"article-title":"SCL-RAI: Span-based contrastive learning with retrieval augmented inference for unlabeled entity problem in NER","year":"2022","author":"Si","key":"ref24"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.810"},{"key":"ref26","first-page":"2159","article-title":"Distantly supervised NER with partial annotation learning and reinforcement learning","volume-title":"Proc. 27th Int. Conf. Comput. Linguistics","author":"Yang"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.371"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.239"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.85"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.710"},{"article-title":"PromptNER: Prompting for named entity recognition","year":"2023","author":"Ashok","key":"ref31"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.findings-naacl.239"},{"article-title":"Instructuie: Multi-task instruction tuning for unified information extraction","year":"2023","author":"Wang","key":"ref33"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3115\/1119176.1119195"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3115\/1596374.1596399"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baw068"},{"key":"ref37","first-page":"27 730","article-title":"Training language models to follow instructions with human feedback","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ouyang"},{"article-title":"Llama: Open and efficient foundation language models","year":"2023","author":"Touvron","key":"ref38"},{"key":"ref39","first-page":"20 331","article-title":"Early-learning regularization prevents memorization of noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref40","first-page":"7164","article-title":"How does disagreement help generalization against label corruption?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu"},{"article-title":"SelfMix: Robust learning against textual label noise with self-mixup training","year":"2022","author":"Qiao","key":"ref41"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12125"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639301"},{"article-title":"RoBERTa: A robustly optimized BERT pretraining approach","year":"2019","author":"Liu","key":"ref44"},{"article-title":"Decoupled weight decay regularization","year":"2017","author":"Loshchilov","key":"ref45"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/11072530\/10988654.pdf?arnumber=10988654","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T21:54:53Z","timestamp":1756245293000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10988654\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":45,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2025.3567204","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"type":"print","value":"1041-4347"},{"type":"electronic","value":"1558-2191"},{"type":"electronic","value":"2326-3865"}],"subject":[],"published":{"date-parts":[[2025,8]]}}}