{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T03:50:16Z","timestamp":1782359416510,"version":"3.54.5"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["SQ2020YFB1600702"],"award-info":[{"award-number":["SQ2020YFB1600702"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFF0501102"],"award-info":[{"award-number":["2021YFF0501102"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52172322"],"award-info":[{"award-number":["52172322"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015860","name":"Foundation of China State Railway Group Company Ltd","doi-asserted-by":"publisher","award":["L2021G003"],"award-info":[{"award-number":["L2021G003"]}],"id":[{"id":"10.13039\/100015860","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["L201004"],"award-info":[{"award-number":["L201004"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["L191015"],"award-info":[{"award-number":["L191015"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005023","name":"State Key Laboratory of Rail Traffic Control and Safety","doi-asserted-by":"publisher","award":["RCS2022ZZ003"],"award-info":[{"award-number":["RCS2022ZZ003"]}],"id":[{"id":"10.13039\/501100005023","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005023","name":"State Key Laboratory of Rail Traffic Control and Safety","doi-asserted-by":"publisher","award":["RCS2022ZI002"],"award-info":[{"award-number":["RCS2022ZI002"]}],"id":[{"id":"10.13039\/501100005023","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2022JBQY001"],"award-info":[{"award-number":["2022JBQY001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1109\/tits.2022.3182371","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T19:28:53Z","timestamp":1655494133000},"page":"21201-21215","source":"Crossref","is-referenced-by-count":59,"title":["Adversarial Training Lattice LSTM for Named Entity Recognition of Rail Fault Texts"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8412-9853","authenticated-orcid":false,"given":"Shuai","family":"Su","sequence":"first","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2827-5562","authenticated-orcid":false,"given":"Jia","family":"Qu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6631-4908","authenticated-orcid":false,"given":"Yuan","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruoqing","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7739-7945","authenticated-orcid":false,"given":"Guang","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3115\/1220575.1220666"},{"key":"ref35","first-page":"2672","article-title":"Generative adversarial netsworks","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3115\/1218955.1219009"},{"key":"ref34","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 (NAACL-HLT)"},{"key":"ref15","first-page":"45","article-title":"Incorporating external annotation to improve named entity translation in NMT","author":"modrzejewski","year":"2020","journal-title":"Proc 22nd Annu Conf Eur Assoc Mach Transl (EAMT)"},{"key":"ref37","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3115\/1690219.1690287"},{"key":"ref36","article-title":"Conditional generative adversarial nets","author":"mirza","year":"2014","journal-title":"arXiv 1411 1784"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-2630"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1161"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981314"},{"key":"ref33","article-title":"TENER: Adapting transformer encoder for named entity recognition","author":"yan","year":"2019","journal-title":"arXiv 1911 04474"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2472580"},{"key":"ref32","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc 31st Conf Neural Inf Process Syst (NIPS)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2019.2907681"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2939358"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3115\/974557.974586"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1017"},{"key":"ref16","first-page":"51","article-title":"Named entity recognition for question answering","author":"molla aliod","year":"2009","journal-title":"Proc Australas Lang Technol Workshop (ALTW)"},{"key":"ref38","first-page":"21","article-title":"Generating text via adversarial training","volume":"21","author":"zhang","year":"2016","journal-title":"Proc NIPS Workshop Adversarial Training"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3115\/1072228.1072282"},{"key":"ref18","first-page":"282","article-title":"Conditional random fields: Probabilistic models for segmenting and labeling sequence data","author":"lafferty","year":"2001","journal-title":"Proc 18th Int Conf Mach Learn (ICML)"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2034"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1967-3_8"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1030"},{"key":"ref45","article-title":"Efficient estimation of word representations in vector space","author":"mikolov","year":"2013","journal-title":"arXiv 1301 3781 [cs]"},{"key":"ref26","article-title":"An attentive sequence model for adverse drug event extraction from biomedical text","author":"ramamoorthy","year":"2018","journal-title":"arXiv 1801 00625"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00104"},{"key":"ref47","author":"carbonell","year":"1990","journal-title":"Machine Learning Paradigms and Methods"},{"key":"ref20","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"collobert","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103481"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103290"},{"key":"ref22","article-title":"Bidirectional LSTM-CRF models for sequence tagging","author":"huang","year":"2015","journal-title":"arXiv 1508 01991"},{"key":"ref44","first-page":"116","article-title":"Structured scenario model for routine emergency of rail transit","volume":"41","author":"yu","year":"2019","journal-title":"Logistics engineering and management"},{"key":"ref21","article-title":"Generating sequences with recurrent neural networks","author":"graves","year":"2013"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICRIS52159.2020.00096"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1144"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1236"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.611"},{"key":"ref8","article-title":"Analyzing the causation of a railway accident based on a complex network","volume":"23","author":"xin","year":"2014","journal-title":"Chin Phys B"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.10.011"},{"key":"ref9","first-page":"53","article-title":"Text mining based fault diagnosis of vehicle on-board equipment for high speed railway","volume":"37","author":"yang","year":"2015","journal-title":"J China Railway Soc"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00235"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2021.103249"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2019.8917094"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3321619.3321623"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11507"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/9942712\/09800123.pdf?arnumber=9800123","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T16:30:22Z","timestamp":1683822622000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9800123\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11]]},"references-count":47,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tits.2022.3182371","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11]]}}}