{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:46:27Z","timestamp":1769553987317,"version":"3.49.0"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korea government","award":["NRF2016M3C4A7952587"],"award-info":[{"award-number":["NRF2016M3C4A7952587"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE\/ACM Trans. Audio Speech Lang. Process."],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/taslp.2019.2948773","type":"journal-article","created":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T19:32:23Z","timestamp":1571686343000},"page":"105-115","source":"Crossref","is-referenced-by-count":10,"title":["Learning Context Using Segment-Level LSTM for Neural Sequence Labeling"],"prefix":"10.1109","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-9057","authenticated-orcid":false,"given":"Youhyun","family":"Shin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sang-goo","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"465","article-title":"Improving the scalability of semi-Markov conditional random fields for named entity recognition","author":"okanohara","year":"0","journal-title":"Proc 21st Int Conf Comput Linguist 44th Ann Meeting Assoc Comput Linguist"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-2038"},{"key":"ref33","first-page":"1","article-title":"Segmental recurrent neural networks","author":"kong","year":"0","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref32","first-page":"1185","article-title":"Semi-Markov conditional random fields for information extraction","author":"sarawagi","year":"0","journal-title":"Proc 17th Int Conf Neural Inf Process Syst"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1531"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1645"},{"key":"ref37","first-page":"97","article-title":"Segment-level neural conditional random fields for named entity recognition","author":"sato","year":"0","journal-title":"Proc 8th Int Joint Conf Natural Lang Process"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1134"},{"key":"ref35","first-page":"2880","article-title":"Exploring segment representations for neural segmentation models","author":"liu","year":"0","journal-title":"Proc 25th Int Joint Conf Artif Intell"},{"key":"ref34","first-page":"2736","article-title":"Neural semi-Markov conditional random fields for robust character-based part-of-speech tagging","author":"kemos","year":"0","journal-title":"Proc Conf North Am Chap Assoc Comput Linguist Human Lang Technol"},{"key":"ref28","article-title":"Bidirectional LSTM-CRF models for sequence tagging","author":"huang","year":"2015","journal-title":"arXiv 1508 01991"},{"key":"ref27","first-page":"1310","article-title":"On the difficulty of training recurrent neural networks","author":"pascanu","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"ref2","first-page":"282","article-title":"Conditional random fields: Probabilistic models for segmenting and labeling sequence data","author":"lafferty","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.1989.266451"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1996.548916"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.1999.758175"},{"key":"ref24","first-page":"850","article-title":"Learning to forget: Continual prediction with LSTM","author":"gers","year":"0","journal-title":"Proc 9th Int Conf Artif Neural Netw"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3115\/1614049.1614064"},{"key":"ref51","first-page":"143","article-title":"Towards robust linguistic analysis using ontonotes","author":"pradhan","year":"0","journal-title":"Proc Computational Natural Language Learning"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.11"},{"key":"ref58","first-page":"2635","article-title":"One billion word benchmark for measuring progress in statistical language modeling","author":"chelba","year":"0","journal-title":"Proc INTERSPEECH"},{"key":"ref57","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"0","journal-title":"Proc Conf North Am Chap Assoc Comput Linguist Human Lang Technol"},{"key":"ref56","first-page":"1","article-title":"Transfer learning for sequence tagging with hierarchical recurrent networks","author":"yang","year":"0","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref55","article-title":"Automatic differentiation in pytorch","author":"paszke","year":"0","journal-title":"Proc Neural Inf Process Syst Workshop Autodiff"},{"key":"ref54","article-title":"Enhancing of chemical compound and drug name recognition using representative tag scheme and fine-grained tokenization","volume":"7","author":"dai","year":"2015","journal-title":"Journal of Chemistry"},{"key":"ref53","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"hinton","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref52","first-page":"1","article-title":"CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in ontonotes","author":"pradhan","year":"0","journal-title":"Proc Joint Conf Conf Empir Methods Natural Lang Proc CoNLL-Shared Task"},{"key":"ref10","first-page":"78","article-title":"Lexicon infused phrase embeddings for named entity resolution","author":"passos","year":"0","journal-title":"Proc Conf Computational Natural Language Learning"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1104"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014065"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W15-3904"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00104"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1101"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1030"},{"key":"ref16","first-page":"5253","article-title":"Empower sequence labeling with task-aware neural language model","author":"liu","year":"0","journal-title":"Proc Assoc Adv Artif Intell"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1194"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1161"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1206"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3115\/1118693.1118694"},{"key":"ref3","first-page":"171","article-title":"Part-of-speech tagging from 97% to 100%: Is it time for some linguistics?","author":"manning","year":"0","journal-title":"Proc Comput Linguist Intell Text Process"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3115\/1596374.1596399"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3115\/1073445.1073478"},{"key":"ref8","first-page":"48","article-title":"Semisupervised condensed nearest neighbor for part-of-speech tagging","author":"s\u00f8gaard","year":"0","journal-title":"Proc 49th Annu Meeting Assoc Comput Linguist Human Lang Technol"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3115\/1690219.1690290"},{"key":"ref49","first-page":"142","article-title":"Introduction to the CONLL-2003 shared task: Language-independent named entity recognition","author":"sang","year":"0","journal-title":"Proc Computational Natural Language Learning"},{"key":"ref9","first-page":"2402","article-title":"Structure regularization for structured prediction","author":"sun","year":"0","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref46","article-title":"Empirical evaluation of rectified activations in convolutional network","author":"xu","year":"0","journal-title":"Proc Int Conf Mach Learn Deep Learn Workshop"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref48","first-page":"127","article-title":"Introduction to the CONLL-2000 shared task: Chunking","author":"sang","year":"0","journal-title":"Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning -"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.21236\/ADA273556"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281288"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3115\/1610075.1610140"},{"key":"ref44","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","author":"mikolov","year":"0","journal-title":"Proc 26th Int Conf Neural Inf Process Syst"},{"key":"ref43","first-page":"1335","article-title":"Extracting opinion expressions with semi-Markov conditional random fields","author":"yang","year":"0","journal-title":"Proc Joint Conf Conf Empir Methods Natural Lang Proc Comp Natural Lang Learn"}],"container-title":["IEEE\/ACM Transactions on Audio, Speech, and Language Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6570655\/8938144\/08878020.pdf?arnumber=8878020","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T17:31:23Z","timestamp":1651080683000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8878020\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":59,"URL":"https:\/\/doi.org\/10.1109\/taslp.2019.2948773","relation":{},"ISSN":["2329-9290","2329-9304"],"issn-type":[{"value":"2329-9290","type":"print"},{"value":"2329-9304","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}