{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T04:17:08Z","timestamp":1742271428242,"version":"3.40.1"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1109\/tbdata.2024.3410099","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T18:01:07Z","timestamp":1717610467000},"page":"512-523","source":"Crossref","is-referenced-by-count":0,"title":["A Reinforcement Learning Framework for N-Ary Document-Level Relation Extraction"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9667-0460","authenticated-orcid":false,"given":"Chenhan","family":"Yuan","sequence":"first","affiliation":[{"name":"University of Manchester, Manchester, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9758-0635","authenticated-orcid":false,"given":"Ryan","family":"Rossi","sequence":"additional","affiliation":[{"name":"Adobe Research, San Jose, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3554-9015","authenticated-orcid":false,"given":"Andrew","family":"Katz","sequence":"additional","affiliation":[{"name":"Virginia Tech, Blacksburg, VA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9712-6667","authenticated-orcid":false,"given":"Hoda","family":"Eldardiry","sequence":"additional","affiliation":[{"name":"Virginia Tech, Blacksburg, VA, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1395"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016513"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/2629489"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1370"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1024"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00049"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-020-03629-9"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.300"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/2509558.2509571"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10020624"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.bionlp-1.7"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.127"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i16.17667"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111410"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47426-3_16"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.582"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i16.17665"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-acl.132"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.5220\/0005187303170324"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D13-1043"},{"article-title":"Combining long short term memory and convolutional neural network for cross-sentence n-ary relation extraction","volume-title":"Proc. Conf. Automated Knowl. Base Construction","author":"Mandya","key":"ref21"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1246"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3115\/1690219.1690287"},{"key":"ref24","first-page":"721","article-title":"Reducing wrong labels in distant supervision for relation extraction","volume-title":"Proc. 50th Annu. Meeting Assoc. Comput. Linguistics","author":"Takamatsu"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463103"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1046"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12063"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1199"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1325"},{"key":"ref31","first-page":"1057","article-title":"Policy gradient methods for reinforcement learning with function approximation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Sutton"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.13140\/RG.2.2.18893.74727"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p16-1200"},{"article-title":"Representation learning with contrastive predictive coding","year":"2018","author":"Oord","key":"ref35"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1074"},{"key":"ref37","first-page":"3190","article-title":"A dataset for n-ary relation extraction of drug combinations","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol.","author":"Tiktinsky"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1189"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3241741"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.147"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110873"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110428"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1159"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n19-1423"},{"article-title":"RoBERTa: A robustly optimized BERT pretraining approach","year":"2019","author":"Liu","key":"ref45"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3289879"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104445"},{"key":"ref48","first-page":"38","article-title":"Transformers: State-of-the-art natural language processing","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process. Syst. Demonstrations","author":"Wolf"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6687317\/10925345\/10549760.pdf?arnumber=10549760","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T21:17:58Z","timestamp":1742246278000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10549760\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":48,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2024.3410099","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"type":"electronic","value":"2332-7790"},{"type":"electronic","value":"2372-2096"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}