{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T11:32:51Z","timestamp":1767007971593,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819996131"},{"type":"electronic","value":"9789819996148"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-9614-8_9","type":"book-chapter","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T15:02:31Z","timestamp":1704294151000},"page":"133-146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Chinese Named Entity Recognition Within the\u00a0Electric Power Domain"],"prefix":"10.1007","author":[{"given":"Jun","family":"Feng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongkai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangying","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yidan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haomin","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongju","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,4]]},"reference":[{"issue":"1","key":"9_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1075\/li.30.1.03nad","volume":"30","author":"D Nadeau","year":"2007","unstructured":"Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3\u201326 (2007)","journal-title":"Lingvisticae Investigationes"},{"issue":"7","key":"9_CR2","first-page":"48","volume":"29","author":"T Xie","year":"2020","unstructured":"Xie, T., Yang, J.A., Liu, H.: Chinese entity recognition based on BERT-BiLSTM-CRF model. Comput. Syst. Appl. 29(7), 48\u201357 (2020)","journal-title":"Comput. Syst. Appl."},{"issue":"2","key":"9_CR3","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","volume":"33","author":"S Ji","year":"2021","unstructured":"Ji, S., Pan, S., Cambria, E., et al.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33(2), 494\u2013514 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882(2014)","DOI":"10.3115\/v1\/D14-1181"},{"issue":"2","key":"9_CR5","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179\u2013211 (1990)","journal-title":"Cogn. Sci."},{"issue":"8","key":"9_CR6","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long Short-Term Memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"9_CR7","unstructured":"Chung, J., Gulcehre, C., Cho, K.H., et al.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"key":"9_CR8","unstructured":"Devlin, J., Chang, M.W., Lee, K., et al.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"issue":"2","key":"9_CR9","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1007\/s10462-022-10197-2","volume":"56","author":"M Baigang","year":"2023","unstructured":"Baigang, M., Fan, Y.: A review: development of named entity recognition (NER) technology for aeronautical information intelligence. Artif. Intell. Rev. 56(2), 1515\u20131542 (2023)","journal-title":"Artif. Intell. Rev."},{"issue":"16","key":"9_CR10","first-page":"1","volume":"57","author":"KN Jiao","year":"2021","unstructured":"Jiao, K.N., Li, X., Zhu, R.C.: A review of named entity recognition in Chinese domain. Comput. Eng. Appl. 57(16), 1\u201315 (2021)","journal-title":"Comput. Eng. Appl."},{"issue":"3","key":"9_CR11","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/S0959-440X(96)80056-X","volume":"6","author":"SR Eddy","year":"1996","unstructured":"Eddy, S.R.: Hidden Markov models. Curr. Opin. Struct. Biol. 6(3), 361\u2013365 (1996)","journal-title":"Curr. Opin. Struct. Biol."},{"issue":"1","key":"9_CR12","first-page":"39","volume":"22","author":"A Berger","year":"1996","unstructured":"Berger, A., Della Pietra, S.A., Della Pietra, V.J.: A maximum entropy approach to natural language processing. Comput. Linguist. 22(1), 39\u201371 (1996)","journal-title":"Comput. Linguist."},{"key":"9_CR13","unstructured":"Isozaki, H., Kazawa, H.: Speeding up named entity recognition based on support vector machines. IPSJ SIG Notes NL-149 1, 1\u20138 (2002)"},{"key":"9_CR14","unstructured":"Lafferty, J., Mccallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)"},{"key":"9_CR15","first-page":"2493","volume":"12","author":"R Collobert","year":"2011","unstructured":"Collobert, R., Weston, J., Bottou, L., et al.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493\u20132537 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"9_CR16","unstructured":"Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Ma, X. Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNS-CRF. arXiv preprint arXiv:1603.01354 (2016)","DOI":"10.18653\/v1\/P16-1101"},{"issue":"3","key":"9_CR18","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1109\/TNB.2019.2908678","volume":"18","author":"J Qiu","year":"2019","unstructured":"Qiu, J., Zhou, Y., Wang, Q., et al.: Chinese clinical named entity recognition using residual dilated convolutional neural network with conditional random field. IEEE Trans. Nanobiosci. 18(3), 306\u2013315 (2019)","journal-title":"IEEE Trans. Nanobiosci."},{"key":"9_CR19","unstructured":"Yan, H., Deng, B., Li, X., et al.: TENER: adapting transformer encoder for named entity recognition. arXiv preprint arXiv:1911.04474 (2019)"},{"issue":"3","key":"9_CR20","first-page":"159","volume":"38","author":"QX Zeng","year":"2021","unstructured":"Zeng, Q.X., Xiong, W.P., Du, J.Q., et al.: Named entity recognition of electronic medical records with BiLSTM-CRF combined with self-attention. Comput. Appl. Softw. 38(3), 159\u2013162 (2021)","journal-title":"Comput. Appl. Softw."},{"issue":"4","key":"9_CR21","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/s12145-019-00390-3","volume":"12","author":"Q Qiu","year":"2019","unstructured":"Qiu, Q., Xie, Z., Wu, L., et al.: BiLSTM-CRF for geological named entity recognition from the geoscience literature. Earth Sci. Inf. 12(4), 565\u2013579 (2019)","journal-title":"Earth Sci. Inf."},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., et al.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603:01360 (2016)","DOI":"10.18653\/v1\/N16-1030"},{"key":"9_CR23","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Advances in Neural Information Processing System, vol. 30 (2017)"},{"key":"9_CR24","unstructured":"Xu, L., Tong, Y., Dong, Q., et al.: CLUENER2020: fine-grained named entity recognition dataset and benchmark for Chinese. arXiv preprint arXiv:2001.04351 (2020)"}],"container-title":["Communications in Computer and Information Science","Emerging Information Security and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-9614-8_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T15:04:29Z","timestamp":1704294269000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-9614-8_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819996131","9789819996148"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-9614-8_9","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"4 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EISA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Emerging Information Security and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eisa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eisa.compute.dtu.dk\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}