{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T10:00:00Z","timestamp":1784887200528,"version":"3.55.0"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,8,28]],"date-time":"2021-08-28T00:00:00Z","timestamp":1630108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,8,28]],"date-time":"2021-08-28T00:00:00Z","timestamp":1630108800000},"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":["Computing"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00607-021-01000-1","type":"journal-article","created":{"date-parts":[[2021,8,28]],"date-time":"2021-08-28T07:06:45Z","timestamp":1630134405000},"page":"979-997","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multicore based least confidence query sampling strategy to speed up active learning approach for named entity recognition"],"prefix":"10.1007","volume":"105","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5662-3237","authenticated-orcid":false,"given":"Ankit","family":"Agrawal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarsij","family":"Tripathi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manu","family":"Vardhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,28]]},"reference":[{"key":"1000_CR1","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/s11128-019-2460-x","volume":"18","author":"Z He","year":"2019","unstructured":"He Z, Li L, Zheng S, Zou X, Situ H (2019) Quantum speedup for pool-based active learning. Quantum Inf Process 18:345. https:\/\/doi.org\/10.1007\/s11128-019-2460-x","journal-title":"Quantum Inf Process"},{"key":"1000_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00429ED1V01Y201207AIM018","volume":"6","author":"B Settles","year":"2012","unstructured":"Settles B (2012) Active learning. Synth Lect Artif Intell Mach Learn 6:1\u2013114. https:\/\/doi.org\/10.2200\/S00429ED1V01Y201207AIM018","journal-title":"Synth Lect Artif Intell Mach Learn"},{"key":"1000_CR3","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s11390-020-9487-4","volume":"35","author":"P Kumar","year":"2020","unstructured":"Kumar P, Gupta A (2020) Active learning query strategies for classification, regression, and clustering: a survey. J Comput Sci Technol 35:913\u2013945. https:\/\/doi.org\/10.1007\/s11390-020-9487-4","journal-title":"J Comput Sci Technol"},{"key":"1000_CR4","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/978-981-15-3125-5_18","volume-title":"Advances in cybernetics, cognition, and machine learning for communication technologies","author":"A Agrawal","year":"2020","unstructured":"Agrawal A, Tripathi S (2020) Active learning using margin sampling strategy for entity recognition. In: Gunjan VK, Senatore S, Kumar A, Gao X-Z, Merugu S (eds) Advances in cybernetics, cognition, and machine learning for communication technologies. Springer, Singapore, pp 163\u2013169"},{"key":"1000_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-021-00230-w","author":"A Agrawal","year":"2021","unstructured":"Agrawal A, Tripathi S, Vardhan M (2021) Active learning approach using a modified least confidence sampling strategy for named entity recognition. Prog Artif Intell. https:\/\/doi.org\/10.1007\/s13748-021-00230-w","journal-title":"Prog Artif Intell"},{"key":"1000_CR6","doi-asserted-by":"publisher","first-page":"99","DOI":"10.3233\/IDT-200048","volume":"15","author":"A Agrawal","year":"2021","unstructured":"Agrawal A, Tripathi S, Vardhan M (2021) Uncertainty query sampling strategies for active learning of named entity recognition task. Intell Decision Technol 15:99\u2013114. https:\/\/doi.org\/10.3233\/IDT-200048","journal-title":"Intell Decision Technol"},{"key":"1000_CR7","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1007\/s00607-019-00748-x","volume":"102","author":"A Alokaili","year":"2020","unstructured":"Alokaili A, Menai MEB (2020) SVM ensembles for named entity disambiguation. Computing 102:1051\u20131076. https:\/\/doi.org\/10.1007\/s00607-019-00748-x","journal-title":"Computing"},{"key":"1000_CR8","doi-asserted-by":"crossref","unstructured":"Zhao Y, Zhang H, Zhou S, Zhang Z (2020) Active learning approaches to enhancing neural machine translation. In: Findings of the association for computational linguistics: EMNLP 2020. Association for Computational Linguistics, Online, pp 1796\u20131806","DOI":"10.18653\/v1\/2020.findings-emnlp.162"},{"key":"1000_CR9","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1007\/s00607-019-00752-1","volume":"102","author":"Y Xia","year":"2020","unstructured":"Xia Y (2020) Research on statistical machine translation model based on deep neural network. Computing 102:643\u2013661. https:\/\/doi.org\/10.1007\/s00607-019-00752-1","journal-title":"Computing"},{"key":"1000_CR10","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s00607-019-00755-y","volume":"102","author":"Z Jiang","year":"2020","unstructured":"Jiang Z, Gao S, Chen L (2020) Study on text representation method based on deep learning and topic information. Computing 102:623\u2013642. https:\/\/doi.org\/10.1007\/s00607-019-00755-y","journal-title":"Computing"},{"key":"1000_CR11","doi-asserted-by":"publisher","DOI":"10.1145\/3439726","author":"S Minaee","year":"2021","unstructured":"Minaee S, Kalchbrenner N, Cambria E, Nikzad N, Chenaghlu M, Gao J (2021) Deep learning based text classification: a comprehensive review. ACM Comput Surv. https:\/\/doi.org\/10.1145\/3439726","journal-title":"ACM Comput Surv"},{"key":"1000_CR12","doi-asserted-by":"crossref","unstructured":"Shen Y, Yun H, Lipton ZC, Kronrod Y, Anandkumar A (2017) Deep active learning for named entity recognition. CoRR abs\/1707.0","DOI":"10.18653\/v1\/W17-2630"},{"key":"1000_CR13","doi-asserted-by":"crossref","unstructured":"Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, Stroudsburg, pp 1070\u20131079","DOI":"10.3115\/1613715.1613855"},{"key":"1000_CR14","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s13042-014-0275-8","volume":"7","author":"A Ekbal","year":"2016","unstructured":"Ekbal A, Saha S, Sikdar UK (2016) On active annotation for named entity recognition. Int J Mach Learn Cybern 7:623\u2013640. https:\/\/doi.org\/10.1007\/s13042-014-0275-8","journal-title":"Int J Mach Learn Cybern"},{"key":"1000_CR15","unstructured":"Liu M, Tu Z, Wang Z, Xu X (2020) LTP: a new active learning strategy for bert-CRF based named entity recognition. http:\/\/arxiv.org\/abs\/1707.05928"},{"key":"1000_CR16","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1155\/2018\/1890683","volume":"2018","author":"H Huang","year":"2018","unstructured":"Huang H, Wang H, Jin D (2018) A low-cost named entity recognition research based on active learning. Sci Program 2018:10. https:\/\/doi.org\/10.1155\/2018\/1890683","journal-title":"Sci Program"},{"key":"1000_CR17","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1007\/978-3-319-54472-4_25","volume-title":"Intelligent information and database systems","author":"VC Tran","year":"2017","unstructured":"Tran VC, Hoang DT, Nguyen NT, Hwang D (2017) A hybrid method for named entity recognition on tweet streams. In: Nguyen NT, Tojo S, Nguyen LM, Trawi\u0144ski B (eds) Intelligent information and database systems. Springer, Cham, pp 258\u2013268"},{"key":"1000_CR18","doi-asserted-by":"crossref","unstructured":"Tsymbalov E, Makarychev S, Shapeev A, Panov M (2019) Deeper connections between neural networks and Gaussian processes speed up active learning. CoRR. abs\/1902.1","DOI":"10.24963\/ijcai.2019\/499"},{"key":"1000_CR19","unstructured":"Sang KTEF, De Meulder F (2003) Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In: Proceedings of the 7th conference on natural language learning at HLT-NAACL. Association for Computational Linguistics, pp 142\u2013147"},{"key":"1000_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jbi.2013.12.006","volume":"47","author":"RI Do\u011fan","year":"2014","unstructured":"Do\u011fan RI, Leaman R, Lu Z (2014) NCBI disease corpus: a resource for disease name recognition and concept normalization. J Biomed Inform 47:1\u201310. https:\/\/doi.org\/10.1016\/j.jbi.2013.12.006","journal-title":"J Biomed Inform"},{"key":"1000_CR21","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baw068","author":"J Li","year":"2016","unstructured":"Li J, Sun Y, Johnson RJ, Sciaky D, Wei C-H, Leaman R, Davis AP, Mattingly CJ, Wiegers TC, Lu Z (2016) BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database. https:\/\/doi.org\/10.1093\/database\/baw068","journal-title":"Database"},{"key":"1000_CR22","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1186\/s12859-017-1776-8","volume":"18","author":"G Crichton","year":"2017","unstructured":"Crichton G, Pyysalo S, Chiu B, Korhonen A (2017) A neural network multi-task learning approach to biomedical named entity recognition. BMC Bioinform 18:368. https:\/\/doi.org\/10.1186\/s12859-017-1776-8","journal-title":"BMC Bioinform"},{"key":"1000_CR23","unstructured":"Cancer Genetics (CG) task: BioNLP-ST 2013. http:\/\/2013.bionlp-st.org\/tasks\/cancer-genetics"},{"key":"1000_CR24","doi-asserted-by":"crossref","unstructured":"Settles B (2004) Biomedical named entity recognition using conditional random fields and rich feature sets. In: Proceedings of the international joint workshop on natural language processing in biomedicine and its applications. Association for Computational Linguistics, Stroudsburg, pp 104\u2013107","DOI":"10.3115\/1567594.1567618"},{"key":"1000_CR25","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.jbi.2015.09.010","volume":"58","author":"Y Chen","year":"2015","unstructured":"Chen Y, Lasko TA, Mei Q, Denny JC, Xu H (2015) A study of active learning methods for named entity recognition in clinical text. J Biomed Inform 58:11\u201318. https:\/\/doi.org\/10.1016\/j.jbi.2015.09.010","journal-title":"J Biomed Inform"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-021-01000-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-021-01000-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-021-01000-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T15:06:09Z","timestamp":1682348769000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-021-01000-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,28]]},"references-count":25,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["1000"],"URL":"https:\/\/doi.org\/10.1007\/s00607-021-01000-1","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,28]]},"assertion":[{"value":"12 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}