{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:05:45Z","timestamp":1774631145252,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2018YFC0407901"],"award-info":[{"award-number":["2018YFC0407901"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012154","name":"Graduate Research and Innovation Projects of Jiangsu Province","doi-asserted-by":"publisher","award":["2019B64214"],"award-info":[{"award-number":["2019B64214"]}],"id":[{"id":"10.13039\/501100012154","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009558","name":"University Natural Science Research Project of Anhui Province","doi-asserted-by":"publisher","award":["KJ2019A1277"],"award-info":[{"award-number":["KJ2019A1277"]}],"id":[{"id":"10.13039\/501100009558","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s00521-021-06685-1","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:03:47Z","timestamp":1642377827000},"page":"6397-6412","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Joint extraction of entities and relations using multi-label tagging and relational alignment"],"prefix":"10.1007","volume":"34","author":[{"given":"Tingting","family":"Hang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9302-0358","authenticated-orcid":false,"given":"Jun","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Le","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Yunfeng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jiamin","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"6685_CR1","unstructured":"Golshan PN, Dashti HR, Azizi S, Safari L (2018) A study of recent contributions on information extraction. arXiv preprint http:\/\/arxiv.org\/abs\/180305667"},{"issue":"1","key":"6685_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1162\/COLI\\_a_00178","volume":"30","author":"D Nadeau","year":"2007","unstructured":"Nadeau D, Sekine S (2007) A survey of named entity recognition and classification. Lingvist Investig 30(1):3\u201326. https:\/\/doi.org\/10.1162\/COLI_a_00178","journal-title":"Lingvist Investig"},{"key":"6685_CR3","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.ins.2019.10.065","volume":"513","author":"H Fei","year":"2020","unstructured":"Fei H, Ren Y, Ji D (2020) Dispatched attention with multi-task learning for nested mention recognition. Inf Sci 513:241\u2013251. https:\/\/doi.org\/10.1016\/j.ins.2019.10.065","journal-title":"Inf Sci"},{"key":"6685_CR4","unstructured":"Rink B, Harabagiu S (2010) Utd: Classifying semantic relations by combining lexical and semantic resources. In: Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval), pp 256\u2013259, https:\/\/www.aclweb.org\/anthology\/S10-1057"},{"key":"6685_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105928","author":"H Wang","year":"2020","unstructured":"Wang H, Qin K, Lu G, Luo G, Liu G (2020) Direction-sensitive relation extraction using bi-sdp attention model. Knowl Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2020.105928","journal-title":"Knowl Based Syst"},{"key":"6685_CR6","doi-asserted-by":"crossref","unstructured":"Li Q, Ji H (2014) Incremental joint extraction of entity mentions and relations. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), pp 402\u2013412, https:\/\/www.aclweb.org\/anthology\/P14-1038","DOI":"10.3115\/v1\/P14-1038"},{"key":"6685_CR7","doi-asserted-by":"publisher","unstructured":"Singh S, Riedel S, Martin B, Zheng J, McCallum A (2013) Joint inference of entities, relations, and coreference. In: Proceedings of the 2013 Workshop on Automated Knowledge Base Construction (AKBC), pp 1\u20136, https:\/\/doi.org\/10.1145\/2509558.2509559","DOI":"10.1145\/2509558.2509559"},{"key":"6685_CR8","doi-asserted-by":"crossref","unstructured":"Miwa M, Sasaki Y (2014) Modeling joint entity and relation extraction with table representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1858\u20131869, https:\/\/www.aclweb.org\/anthology\/D14-1200","DOI":"10.3115\/v1\/D14-1200"},{"key":"6685_CR9","doi-asserted-by":"publisher","unstructured":"Ren X, Wu Z, He W, Qu M, Voss CR, Ji H, Abdelzaher TF, Han J (2017) Cotype: Joint extraction of typed entities and relations with knowledge bases. In: Proceedings of the 26th International Conference on World Wide Web (WWW), pp 1015\u20131024, https:\/\/doi.org\/10.1145\/3038912.3052708","DOI":"10.1145\/3038912.3052708"},{"key":"6685_CR10","doi-asserted-by":"crossref","unstructured":"Miwa M, Bansal M (2016) End-to-end relation extraction using lstms on sequences and tree structures. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pp 1105\u20131116, https:\/\/www.aclweb.org\/anthology\/P16-1105","DOI":"10.18653\/v1\/P16-1105"},{"key":"6685_CR11","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.knosys.2016.09.019","volume":"114","author":"S Zheng","year":"2016","unstructured":"Zheng S, Xu J, Zhou P, Bao H, Qi Z, Xu B (2016) A neural network framework for relation extraction: learning entity semantic and relation pattern. Knowl Based Syst 114:12\u201323. https:\/\/doi.org\/10.1016\/j.knosys.2016.09.019","journal-title":"Knowl Based Syst"},{"key":"6685_CR12","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.neucom.2016.12.075","volume":"257","author":"S Zheng","year":"2017","unstructured":"Zheng S, Hao Y, Lu D, Bao H, Xu J, Hao H, Xu B (2017) Joint entity and relation extraction based on a hybrid neural network. Neurocomputing 257:59\u201366. https:\/\/doi.org\/10.1016\/j.neucom.2016.12.075","journal-title":"Neurocomputing"},{"key":"6685_CR13","doi-asserted-by":"publisher","unstructured":"Wang S, Zhang Y, Che W, Liu T (2018) Joint extraction of entities and relations based on a novel graph scheme. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), pp 4461\u20134467, https:\/\/doi.org\/10.24963\/ijcai.2018\/620","DOI":"10.24963\/ijcai.2018\/620"},{"issue":"12","key":"6685_CR14","doi-asserted-by":"publisher","first-page":"9113","DOI":"10.1007\/s00521-019-04430-3","volume":"31","author":"M Lei","year":"2019","unstructured":"Lei M, Huang H, Feng C, Gao Y, Su C (2019) An input information enhanced model for relation extraction. Neural Comput Appl 31(12):9113\u20139126. https:\/\/doi.org\/10.1007\/s00521-019-04430-3","journal-title":"Neural Comput Appl"},{"key":"6685_CR15","doi-asserted-by":"publisher","first-page":"6300","DOI":"10.1609\/aaai.v33i01.33016300","volume":"33","author":"D Dai","year":"2019","unstructured":"Dai D, Xiao X, Lyu Y, Dou S, She Q, Wang H (2019) Joint extraction of entities and overlapping relations using position-attentive sequence labeling. Proc AAAI Conf Artif Intell (AAAI) 33:6300\u20136308. https:\/\/doi.org\/10.1609\/aaai.v33i01.33016300","journal-title":"Proc AAAI Conf Artif Intell (AAAI)"},{"key":"6685_CR16","doi-asserted-by":"publisher","unstructured":"Zheng S, Wang F, Bao H, Hao Y, Zhou P, Xu B (2017) Joint extraction of entities and relations based on a novel tagging scheme. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), pp 1227\u20131236, https:\/\/doi.org\/10.18653\/v1\/P17-1113","DOI":"10.18653\/v1\/P17-1113"},{"key":"6685_CR17","doi-asserted-by":"crossref","unstructured":"Ratinov L, Roth D (2009) Design challenges and misconceptions in named entity recognition. In: Proceedings of the 13th Conference on Computational Natural Language Learning (CoNLL), pp 147\u2013155, https:\/\/www.aclweb.org\/anthology\/W09-1119","DOI":"10.3115\/1596374.1596399"},{"key":"6685_CR18","doi-asserted-by":"publisher","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: Encoder-decoder approaches. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 103\u2013111, https:\/\/doi.org\/10.3115\/v1\/W14-4012","DOI":"10.3115\/v1\/W14-4012"},{"key":"6685_CR19","doi-asserted-by":"crossref","unstructured":"Passos A, Kumar V, McCallum A (2014) Lexicon infused phrase embeddings for named entity resolution. In: Proceedings of the 18th Conference on Computational Natural Language Learning (CoNLL), pp 78\u201386, https:\/\/www.aclweb.org\/anthology\/W14-1609","DOI":"10.3115\/v1\/W14-1609"},{"key":"6685_CR20","doi-asserted-by":"crossref","unstructured":"Luo G, Huang X, Lin CY, Nie Z (2015) Joint entity recognition and disambiguation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 879\u2013888, https:\/\/www.aclweb.org\/anthology\/D15-1104","DOI":"10.18653\/v1\/D15-1104"},{"key":"6685_CR21","unstructured":"Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493\u20132537, https:\/\/dl.acm.org\/citation.cfm?id=2078186"},{"key":"6685_CR22","unstructured":"Rei M, Crichton GK, Pyysalo S (2016) Attending to characters in neural sequence labeling models. In: Proceedings of 26th International Conference on Computational Linguistics (COLING), pp 309\u2013318, https:\/\/www.aclweb.org\/anthology\/C16-1030"},{"key":"6685_CR23","doi-asserted-by":"crossref","unstructured":"Kambhatla N (2004) Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. In: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions (ACL), pp 22\u201325, https:\/\/www.aclweb.org\/anthology\/P04-3022","DOI":"10.3115\/1219044.1219066"},{"key":"6685_CR24","unstructured":"Socher R, Huval B, Manning CD, Ng AY (2012) Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp 1201\u20131211, https:\/\/www.aclweb.org\/anthology\/D12-1110\/"},{"key":"6685_CR25","doi-asserted-by":"crossref","unstructured":"Xu Y, Mou L, Li G, Chen Y, Peng H, Jin Z (2015) Classifying relations via long short term memory networks along shortest dependency paths. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1785\u20131794, https:\/\/www.aclweb.org\/anthology\/D15-1206","DOI":"10.18653\/v1\/D15-1206"},{"key":"6685_CR26","unstructured":"Zhang S, Zheng D, Hu X, Yang M (2015) Bidirectional long short-term memory networks for relation classification. In: Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation (PACLIC), pp 73\u201378, https:\/\/www.aclweb.org\/anthology\/Y15-1009\/"},{"key":"6685_CR27","unstructured":"Xu Y, Jia R, Mou L, Li G, Chen Y, Lu Y, Jin Z (2016) Improved relation classification by deep recurrent neural networks with data augmentation. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers (COLING), pp 1461\u20131470, https:\/\/www.aclweb.org\/anthology\/C16-1138\/"},{"key":"6685_CR28","doi-asserted-by":"crossref","unstructured":"Xu K, Feng Y, Huang S, Zhao D (2015) Semantic relation classification via convolutional neural networks with simple negative sampling. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), https:\/\/www.aclweb.org\/anthology\/D15-1062","DOI":"10.18653\/v1\/D15-1062"},{"key":"6685_CR29","doi-asserted-by":"crossref","unstructured":"dos Santos C, Xiang B, Zhou B (2015) Classifying relations by ranking with convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL), pp 626\u2013634, https:\/\/www.aclweb.org\/anthology\/P15-1061","DOI":"10.3115\/v1\/P15-1061"},{"key":"6685_CR30","doi-asserted-by":"crossref","unstructured":"Wang L, Cao Z, De\u00a0Melo G, Liu Z (2016) Relation classification via multi-level attention cnns. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pp 1298\u20131307, https:\/\/www.aclweb.org\/anthology\/P16-1123","DOI":"10.18653\/v1\/P16-1123"},{"key":"6685_CR31","doi-asserted-by":"crossref","unstructured":"Vu NT, Adel H, Gupta P, Sch\u00fctze H (2016) Combining recurrent and convolutional neural networks for relation classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (ACL), pp 534\u2013539, https:\/\/www.aclweb.org\/anthology\/N16-1065","DOI":"10.18653\/v1\/N16-1065"},{"key":"6685_CR32","doi-asserted-by":"crossref","unstructured":"Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, Xu B (2016) Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pp 207\u2013212, https:\/\/www.aclweb.org\/anthology\/P16-2034","DOI":"10.18653\/v1\/P16-2034"},{"key":"6685_CR33","doi-asserted-by":"publisher","unstructured":"Zheng S, Xu J, Bao H, Qi Z, Zhang J, Hao H, Xu B (2016) Joint learning of entity semantics and relation pattern for relation extraction. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML\/PKDD), pp 443\u2013458, https:\/\/doi.org\/10.1007\/978-3-319-46128-1_28","DOI":"10.1007\/978-3-319-46128-1_28"},{"key":"6685_CR34","doi-asserted-by":"crossref","unstructured":"Katiyar A, Cardie C (2017) Going out on a limb: Joint extraction of entity mentions and relations without dependency trees. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), pp 917\u2013928, https:\/\/www.aclweb.org\/anthology\/P17-1085","DOI":"10.18653\/v1\/P17-1085"},{"key":"6685_CR35","doi-asserted-by":"publisher","first-page":"7080","DOI":"10.1609\/aaai.v33i01.33017080","volume":"33","author":"Z Tan","year":"2019","unstructured":"Tan Z, Zhao X, Wang W, Xiao W (2019) Jointly extracting multiple triplets with multilayer translation constraints. Proc AAAI Conf Artif Intell (AAAI) 33:7080\u20137087. https:\/\/doi.org\/10.1609\/aaai.v33i01.33017080","journal-title":"Proc AAAI Conf Artif Intell (AAAI)"},{"key":"6685_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102311","author":"H Fei","year":"2020","unstructured":"Fei H, Ren Y, Ji D (2020) Boundaries and edges rethinking: an end-to-end neural model for overlapping entity relation extraction. Inf Process Manag 57(6). https:\/\/doi.org\/10.1016\/j.ipm.2020.102311","journal-title":"Inf Process Manag"},{"key":"6685_CR37","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (NIPS), pp 3111\u20133119, http:\/\/papers.nips.cc\/paper\/5021-distributed-representations-of-words-and-phrases-and-theircompositionality"},{"key":"6685_CR38","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in Neural Information Processing Systems (NIPS), pp 5998\u20136008, http:\/\/papers.nips.cc\/paper\/7181-attention-is-all-you-need"},{"key":"6685_CR39","doi-asserted-by":"crossref","unstructured":"Irsoy O, Cardie C (2014) Opinion mining with deep recurrent neural networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 720\u2013728, https:\/\/www.aclweb.org\/anthology\/D14-1080","DOI":"10.3115\/v1\/D14-1080"},{"key":"6685_CR40","unstructured":"Lafferty J, McCallum A, Pereira FC (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning (ICML), pp 282\u2013289"},{"key":"6685_CR41","doi-asserted-by":"publisher","unstructured":"Pinto D, McCallum A, Wei X, Croft WB (2003) Table extraction using conditional random fields. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp 235\u2013242, https:\/\/doi.org\/10.1145\/860435.860479","DOI":"10.1145\/860435.860479"},{"key":"6685_CR42","doi-asserted-by":"publisher","unstructured":"Xue N, Shen L (2003) Chinese word segmentation as lmr tagging. In: Proceedings of the Second Workshop on Chinese Language Processing (SIGHAN), pp 176\u2013179, https:\/\/doi.org\/10.3115\/1119250.1119278","DOI":"10.3115\/1119250.1119278"},{"key":"6685_CR43","unstructured":"Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR), pp 1\u201315, arXiv:abs\/1409.0473"},{"key":"6685_CR44","unstructured":"Ji Y, Zhang H, Jie Z, Ma L, Wu QJ (2020) Casnet: a cross-attention siamese network for video salient object detection. IEEE Trans Neural Networks Learn Syst pp 1\u201315"},{"key":"6685_CR45","doi-asserted-by":"publisher","unstructured":"He S, Liu K, Ji G, Zhao J (2015) Learning to represent knowledge graphs with gaussian embedding. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM), pp 623\u2013632, https:\/\/doi.org\/10.1145\/2806416.2806502","DOI":"10.1145\/2806416.2806502"},{"key":"6685_CR46","unstructured":"Hoffmann R, Zhang C, Ling X, Zettlemoyer L, Weld DS (2011) Knowledge-based weak supervision for information extraction of overlapping relations. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL), pp 541\u2013550, https:\/\/www.aclweb.org\/anthology\/P11-1055"},{"key":"6685_CR47","unstructured":"Ellis J, Li X, Griffitt K, Strassel SM, Wright J (2013) Linguistic resources for 2013 knowledge base population evaluations. In: Proceedings of the Sixth Text Analysis Conference (TAC), https:\/\/tac.nist.gov\/publications\/2013\/additional.papers\/KBP2013_annotation_overview.TAC2013.proceedings.pdf"},{"key":"6685_CR48","doi-asserted-by":"crossref","unstructured":"Liu L, Ren X, Zhu Q, Zhi S, Gui H, Ji H, Han J (2017) Heterogeneous supervision for relation extraction: A representation learning approach. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 46\u201356, https:\/\/www.aclweb.org\/anthology\/D17-1005","DOI":"10.18653\/v1\/D17-1005"},{"key":"6685_CR49","doi-asserted-by":"crossref","unstructured":"Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (ACL), pp 260\u2013270, https:\/\/www.aclweb.org\/anthology\/N16-1030","DOI":"10.18653\/v1\/N16-1030"},{"key":"6685_CR50","unstructured":"Mukkamala MC, Hein M (2017) Variants of rmsprop and adagrad with logarithmic regret bounds. In: Proceedings of the 34th International Conference on Machine Learning (ICML), pp 2545\u20132553, http:\/\/proceedings.mlr.press\/v70\/mukkamala17a.html"},{"key":"6685_CR51","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958, http:\/\/dl.acm.org\/citation.cfm?id=2670313"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06685-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06685-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06685-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T14:27:02Z","timestamp":1647440822000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06685-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,17]]},"references-count":51,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["6685"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06685-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,17]]},"assertion":[{"value":"22 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}