{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T20:57:16Z","timestamp":1762376236320},"reference-count":14,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2017]]},"DOI":"10.1587\/transinf.2016edp7179","type":"journal-article","created":{"date-parts":[[2017,3,31]],"date-time":"2017-03-31T22:23:27Z","timestamp":1490999007000},"page":"882-887","source":"Crossref","is-referenced-by-count":17,"title":["LSTM-CRF Models for Named Entity Recognition"],"prefix":"10.1587","volume":"E100.D","author":[{"given":"Changki","family":"LEE","sequence":"first","affiliation":[{"name":"Department of Computer Science, Kangwon National University"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] T. Mikolov, S. Kombrink, L. Burget, J.H. \u010cernock\u00fd, and S.Khudanpur, \u201cExtensions of recurrent neural network language model,\u201d 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp.5528-5531, 2011.","DOI":"10.1109\/ICASSP.2011.5947611"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] A. Graves, A.R. Mohamed, and G. Hinton, \u201cSpeech recognition with deep recurrent neural networks,\u201d Proc. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp.6645-6649, 2013.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] K. Cho, B. Van Merri\u00ebnboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, \u201cLearning phrase representations using RNN encoder-decoder for statistical machine translation,\u201d arXiv preprint arXiv:1406.1078, 2014.","DOI":"10.3115\/v1\/D14-1179"},{"key":"4","unstructured":"[4] A. Graves, \u201cGenerating sequences with recurrent neural networks,\u201d arXiv preprint arXiv:1308.0850, 2013."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] H. Sak, A.W. Senior, and F. Beaufays, \u201cLong short-term memory recurrent neural network architectures for large scale acoustic modeling,\u201d Proc. INTERSPEECH. 2014.","DOI":"10.21437\/Interspeech.2014-80"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] K. Yao, B. Peng, Y. Zhang, D. Yu, G. Zweig, and Y. Shi, \u201cSpoken language understanding using long short-term memory neural networks,\u201d Proc. 2014 IEEE Spoken Language Technology Workshop (SLT), IEEE, pp.189-194, 2014.","DOI":"10.1109\/SLT.2014.7078572"},{"key":"7","unstructured":"[7] J. Lafferty, A. McCallum, and F.C. Pereira, \u201cConditional random fields: Probabilistic models for segmenting and labeling sequence data,\u201d Proc. 18th International Conference on Machine Learning 2001 (ICML 2001), pp.282-289. 2001."},{"key":"8","unstructured":"[8] R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, \u201cNatural language processing (almost) from scratch,\u201d J. Machine Learning Research, vol.12, pp.2493-2537, 2011."},{"key":"9","unstructured":"[9] T. Mikolov, A. Joulin, S. Chopra, M. Mathieu, and M.A.Ranzato, \u201cLearning longer memory in recurrent neural networks,\u201d arXiv preprint arXiv:1412.7753, 2014."},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] K. Yao, B. Peng, G. Zweig, D. Yu, X. Li, and F. Gao, \u201cRecurrent conditional random field for language understanding,\u201d Proc. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp.4077-4081, 2014.","DOI":"10.1109\/ICASSP.2014.6854368"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] C. Lee, P.M. Ryu, and H. Kim, \u201cNamed entity recognition using a modified Pegasos algorithm,\u201d Proc. 20th ACM international conference on Information and knowledge management, ACM, p.2337, 2011.","DOI":"10.1145\/2063576.2063960"},{"key":"12","unstructured":"[12] F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, and Y. Bengio, \u201cTheano: new features and speed improvements,\u201d arXiv preprint arXiv:1211.5590, 2012."},{"key":"13","unstructured":"[13] G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R.R. Salakhutdinov, \u201cImproving neural networks by preventing co-adaptation of feature detectors,\u201d arXiv preprint arXiv:1207.0580, 2012."},{"key":"14","unstructured":"[14] T. Mikolov, W.T. Yih, and G. Zweig, \u201cLinguistic regularities in continuous space word representations,\u201d Proc. HLT-NAACL, 2013."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E100.D\/4\/E100.D_2016EDP7179\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T23:35:50Z","timestamp":1692747350000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E100.D\/4\/E100.D_2016EDP7179\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":14,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2017]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2016edp7179","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}