{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:15:39Z","timestamp":1785888939121,"version":"3.56.0"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T00:00:00Z","timestamp":1579564800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T00:00:00Z","timestamp":1579564800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s00521-020-04725-w","type":"journal-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T03:02:46Z","timestamp":1579575766000},"page":"2909-2928","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":97,"title":["A deep learning analysis on question classification task using Word2vec representations"],"prefix":"10.1007","volume":"32","author":[{"given":"Seyhmus","family":"Yilmaz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sinan","family":"Toklu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,1,21]]},"reference":[{"key":"4725_CR1","doi-asserted-by":"crossref","unstructured":"Blooma MJ, Goh DHL, Chua AYK, Ling Z (2008) Applying question classification to Yahoo! Answers. In: Applications of digital information and Web Technologies, ICADIWT 2008. First international conference on the IEEE, pp 229\u2013234","DOI":"10.1109\/ICADIWT.2008.4664350"},{"issue":"2","key":"4725_CR2","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10462-010-9188-4","volume":"35","author":"J Silva","year":"2011","unstructured":"Silva J, Lu\u00edsa C, Mendes AC, Andreas W (2011) From symbolic to sub-symbolic information in question classification. Artif Intell Rev 35(2):137\u2013154","journal-title":"Artif Intell Rev"},{"issue":"3","key":"4725_CR3","doi-asserted-by":"publisher","first-page":"39","DOI":"10.5121\/ijwest.2013.4304","volume":"4","author":"M Mishra","year":"2013","unstructured":"Mishra M, Mishra VK, Sharma HR (2013) Question classification using semantic, syntactic and lexical features. Int J Web Semant Technol 4(3):39","journal-title":"Int J Web Semant Technol"},{"key":"4725_CR4","unstructured":"Zhiheng H, Marcus T, Zengchang Q (2008) Question classification using head words and their hypernyms. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 927\u2013936"},{"key":"4725_CR5","unstructured":"Ehsan S, Mojgan F (2014) A hybrid approach for question classification in Persian automatic question answering systems 2014. In: 4th international e conference on computer and knowledge engineering (ICCKE). IEEE, pp 279\u2013284(2014)"},{"key":"4725_CR6","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.cogsys.2017.07.002","volume":"47","author":"M Razzaghnoori","year":"2018","unstructured":"Razzaghnoori M, Sajedi H, Jazani IK (2018) Question classification in Persian using word vectors and frequencies. Cogn Syst Res 47:16\u201327","journal-title":"Cogn Syst Res"},{"key":"4725_CR7","doi-asserted-by":"crossref","unstructured":"Hao T, Xie W, Xu F (2015) A WordNet expansion-based approach for question targets identification and classification. In: Chinese computational linguistics and natural language processing based on naturally annotated Big Data. Springer, Cham, pp 333\u2013344","DOI":"10.1007\/978-3-319-25816-4_27"},{"key":"4725_CR8","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. arxiv preprint: arxiv:1408.5882","DOI":"10.3115\/v1\/D14-1181"},{"issue":"4","key":"4725_CR9","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1007\/s11390-017-1759-2","volume":"32","author":"F Hu","year":"2017","unstructured":"Hu F, Li L, Zhang ZL (2017) Emphasizing essential words for sentiment classification based on recurrent neural networks. J Comput Sci Technol 32(4):785\u2013795. https:\/\/doi.org\/10.1007\/s11390-017-1759-2","journal-title":"J Comput Sci Technol"},{"key":"4725_CR10","unstructured":"https:\/\/www.analyticsvidhya.com\/blog\/2017\/09\/understaing-support-vector-machine-example-code\/"},{"key":"4725_CR11","doi-asserted-by":"crossref","unstructured":"Le-Hong P, Phan XH, Nguyen TD (2015) Using dependency analysis to improve question classification. In Knowledge and Systems Engineering (pp. 653-665). Springer, Cham","DOI":"10.1007\/978-3-319-11680-8_52"},{"key":"4725_CR12","first-page":"85","volume-title":"Technologies of labour and the politics of contradiction","year":"2018","unstructured":"Bili\u0107 P, Primorac J, Valt\u00fdsson B (eds) (2018) Technologies of labour and the politics of contradiction. Springer, Cham, p 85"},{"key":"4725_CR13","unstructured":"\u015eahin G (2017) Turkish document classification based on Word2Vec and SVM classifier. In: Signal processing and communications applications conference (SIU), 2017 25th, IEEE, pp 1\u20134"},{"key":"4725_CR14","unstructured":"http:\/\/user.ceng.metu.edu.tr\/~mturhan\/dfa\/node14.html"},{"key":"4725_CR15","doi-asserted-by":"crossref","unstructured":"Ozturkmenoglu O, Alpkocak A (2012) Comparison of different lemmatization approaches for information retrieval on Turkish text collection. In: 2012 International symposium on innovations in intelligent systems and applications. IEEE","DOI":"10.1109\/INISTA.2012.6246934"},{"issue":"4","key":"4725_CR16","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1007\/s10579-014-9267-2","volume":"48","author":"K Oflazer","year":"2014","unstructured":"Oflazer K (2014) Turkish and its challenges for language processing. Lang Resour Eval 48(4):639\u2013653","journal-title":"Lang Resour Eval"},{"key":"4725_CR17","unstructured":"Wang C (2016) What are the limitations of the Bag-of-Words model? [Online]. Available: https:\/\/www.quora.com\/What-are-the-limitations-of-the-Bag-of-Words-model"},{"key":"4725_CR18","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, pp 3111\u20133119"},{"key":"4725_CR19","unstructured":"Bollegala D, Maehara T, Kawarabayashi K (2015) Embedding semantic relations into word representations. In: Twenty-fourth international joint conference on artificial intelligence"},{"key":"4725_CR20","unstructured":"http:\/\/cogcomp.cs.illinois.edu\/Data\/QA\/QC\/"},{"issue":"4","key":"4725_CR21","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1016\/j.ipm.2019.02.018","volume":"56","author":"A Abdi","year":"2019","unstructured":"Abdi A et al (2019) Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion. Inf Process Manag 56(4):1245\u20131259","journal-title":"Inf Process Manag"},{"key":"4725_CR22","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.procs.2019.01.202","volume":"147","author":"H Ghulam","year":"2019","unstructured":"Ghulam H et al (2019) Deep learning-based sentiment analysis for Roman Urdu text. Procedia Comput Sci 147:131\u2013135","journal-title":"Procedia Comput Sci"},{"key":"4725_CR23","doi-asserted-by":"crossref","unstructured":"Sangkeettrakarn C, Haruechaiyasak C, Theeramunkong T (2019) Fuzziness detection in Thai law texts using deep learning. In:\u00a02019 10th International conference of information and communication technology for embedded systems (IC-ICTES). IEEE","DOI":"10.1109\/ICTEmSys.2019.8695951"},{"key":"4725_CR24","doi-asserted-by":"crossref","unstructured":"Singh J et al (2018) Morphological evaluation and sentiment analysis of Punjabi text using deep learning classification. J King Saud Univ-Comput Inf Sci (2018)","DOI":"10.1016\/j.jksuci.2018.04.003"},{"key":"4725_CR25","doi-asserted-by":"crossref","unstructured":"Nguyen H-Q, Nguyen Q-U (2018) An ensemble of shallow and deep learning algorithms for Vietnamese Sentiment Analysis. In: 2018 5th NAFOSTED conference on information and computer science (NICS). IEEE","DOI":"10.1109\/NICS.2018.8606880"},{"key":"4725_CR26","doi-asserted-by":"crossref","unstructured":"Nguyen T, Shcherbakov M (2018) A neural network based Vietnamese Chatbot. In: 2018 International conference on system modeling & advancement in research trends (SMART). IEEE","DOI":"10.1109\/SYSMART.2018.8746962"},{"key":"4725_CR27","unstructured":"Dmitrin YV (dmitrinyuri@gmail.com), Botov DS Comparison of deep neural network architectures for authorship attribution of Russian Social Media Texts"},{"key":"4725_CR28","doi-asserted-by":"crossref","unstructured":"Vo K et al (2019) Handling negative mentions on social media channels using deep learning.\u00a0J Inf Telecommun 1\u201323","DOI":"10.1080\/24751839.2019.1565652"},{"key":"4725_CR29","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.procs.2018.10.466","volume":"142","author":"Maha Heikal","year":"2018","unstructured":"Heikal Maha, Torki Marwan, El-Makky Nagwa (2018) Sentiment analysis of Arabic Tweets using deep learning. Procedia Comput Sci 142:114\u2013122","journal-title":"Procedia Comput Sci"},{"key":"4725_CR30","doi-asserted-by":"crossref","unstructured":"Hacioglu K, Ward W (2003) Question classification with support vector machines and error correcting codes. In: Proceedings of HLT-NAACL, Association for Computational Linguistics, Morristown, USA, vol 2, pp 28\u201330","DOI":"10.3115\/1073483.1073493"},{"key":"4725_CR31","unstructured":"Loni B (2011) A survey of state-of-the-art methods on question classification"},{"issue":"4","key":"4725_CR32","doi-asserted-by":"publisher","first-page":"31","DOI":"10.5121\/ijwest.2013.4403","volume":"4","author":"PM Athira","year":"2013","unstructured":"Athira PM, Sreeja M, Reghuraj PC (2013) Architecture of an ontology-based domain-specific natural language question answering system. Int J Web Semant Technol 4(4):31","journal-title":"Int J Web Semant Technol"},{"key":"4725_CR33","unstructured":"Hermjakob U (2001) Parsing and question classification for question answering. In: Proceedings of the workshop on open-domain question answering. Association for Computational Linguistics, vol 12, pp 1\u20136"},{"key":"4725_CR34","unstructured":"Close LW (2002) Question classification using language modeling. Center of Intelligent Information Retrieval (CIIR). Technical report"},{"key":"4725_CR35","unstructured":"Dell Z, Wee SL (2003) Question classification using support vector machines. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 26\u201332"},{"key":"4725_CR36","unstructured":"Suzuki J, Taira H, Sasaki Y, Maeda E (2003) Question classification using HDAG kernel. In: Proceedings of the ACL 2003 workshop on multilingual summarization and question answering. Association for Computational Linguistics, vol 12, pp 61\u201368"},{"key":"4725_CR37","unstructured":"Li X, Roth D (2002) Learning question classifiers. In: Proceeding of the 19th international conference on computational linguistics. Association for Computational Linguistics, Morristown, USA, vol 1, pp 1\u20137"},{"key":"4725_CR38","doi-asserted-by":"crossref","unstructured":"Mollaei A, Rahati-Quchani S, Estaji A (2012) Question classification in Persian language based on conditional random fields. In: 2012 2nd international conference on computer and knowledge engineering (ICCKE). IEEE, pp 295\u2013300","DOI":"10.1109\/ICCKE.2012.6395395"},{"key":"4725_CR39","doi-asserted-by":"crossref","unstructured":"Loni BK, Seyedeh H, Wiggers P (2011) Latent semantic analysis for question classification with neural networks. In: 2011 IEEE workshop on automatic speech recognition and understanding (ASRU). IEEE, pp 437\u2013442","DOI":"10.1109\/ASRU.2011.6163971"},{"key":"4725_CR40","doi-asserted-by":"crossref","unstructured":"Blunsom P, Kocik K, Curran J (2006) Question classification with log-linear models. In: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 615\u2013616","DOI":"10.1145\/1148170.1148282"},{"issue":"13","key":"4725_CR41","doi-asserted-by":"publisher","first-page":"1935","DOI":"10.1016\/j.patrec.2010.06.012","volume":"31","author":"K Santosh","year":"2010","unstructured":"Santosh K, Ray Shailendra S, Joshi BP (2010) A semantic approach for question classification using WordNet and Wikipedia. Pattern Recognit Lett 31(13):1935\u20131943","journal-title":"Pattern Recognit Lett"},{"key":"4725_CR42","unstructured":"Lee C-H, Lee H-Y (2019) Cross-lingual transfer learning for question answering. arXiv preprint arXiv:1907.06042"},{"key":"4725_CR43","unstructured":"https:\/\/ahcweb01.naist.jp\/DigRevURL\/index.html"},{"key":"4725_CR44","doi-asserted-by":"crossref","unstructured":"Liu, Jiahua, et al. \u201cXQA: A Cross-lingual Open-domain Question Answering Dataset.\u201d Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019","DOI":"10.18653\/v1\/P19-1227"},{"key":"4725_CR45","unstructured":"Singh J et al (2019) \u201cXLDA: cross-lingual data augmentation for natural language inference and question answering. arXiv preprint arXiv:1905.11471"},{"key":"4725_CR46","unstructured":"Faruqi MI, Purwarianti A (2011) An Indonesian question analyzer to enhance the performance of Indonesian-English CLQA. In: Proceedings of the 2011 international conference on electrical engineering and informatics. IEEE, pp 1\u20136"},{"key":"4725_CR47","doi-asserted-by":"crossref","unstructured":"Sugiyama K, Mizukami M, Neubig G, Yoshino K, Sakti S, Toda T, Nakamura S (2015) An investigation of machine translation evaluation metrics in cross-lingual question answering. In: Proceedings of the tenth workshop on statistical machine translation, pp 442\u2013449","DOI":"10.18653\/v1\/W15-3057"},{"key":"4725_CR48","unstructured":"Gupta D, Kumari S, Ekbal A, Bhattacharyya P (2018 MMQA: a multi-domain multi-lingual question-answering framework for English and Hindi. In: Proceedings of the eleventh international conference on language resources and evaluation (LREC-2018)"},{"key":"4725_CR49","doi-asserted-by":"crossref","unstructured":"Loginova E, Varanasi S, Neumann G (2018) Towards multilingual neural question answering. In: European conference on advances in databases and information systems. Springer, Cham, pp 274\u2013285","DOI":"10.1007\/978-3-030-00063-9_26"},{"key":"4725_CR50","doi-asserted-by":"crossref","unstructured":"Ture F, Boschee E (2016) Learning to translate for multilingual question answering. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 573\u2013584","DOI":"10.18653\/v1\/D16-1055"},{"key":"4725_CR51","unstructured":"http:\/\/www.benila.com.tr\/index.php"},{"key":"4725_CR52","unstructured":"Mikolov, Tomas et al (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"4725_CR53","unstructured":"https:\/\/github.com\/akoksal\/turkish-word2Vec"},{"key":"4725_CR54","unstructured":"https:\/\/israelg99.github.io\/2017-03-23-Word2Vec-Explained\/"},{"key":"4725_CR55","unstructured":"https:\/\/medium.com\/@mubuyuk51\/word2vec-nedir-t%C3%BCrk%C3%A7e-f0cfab20d3ae"},{"key":"4725_CR56","unstructured":"Mandelbaum A, Shalev A (2016) Word embeddings and their use in sentence classification tasks. arXiv preprint arXiv:1610.08229"},{"key":"4725_CR57","unstructured":"https:\/\/radimrehurek.com\/gensim\/models\/word2vec.html"},{"issue":"1","key":"4725_CR58","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1109\/TVCG.2017.2745141","volume":"24","author":"S Liu","year":"2018","unstructured":"Liu S, Bremer PT, Thiagarajan JJ, Srikumar V, Wang B, Livnat Y, Pascucci V (2018) Visual exploration of semantic relationships in neural word embeddings. IEEE Trans Vis Comput Graph\u00a024(1):553\u2013562","journal-title":"IEEE Trans Vis Comput Graph"},{"issue":"2","key":"4725_CR59","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1186\/s12911-018-0630-x","volume":"18","author":"Z Chen","year":"2018","unstructured":"Chen Z et al (2018) Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases. BMC Med Inf Decis Mak 18(2):65","journal-title":"BMC Med Inf Decis Mak"},{"key":"4725_CR60","unstructured":"https:\/\/medium.com\/@patrickhk\/practice-ntlk-word2vec-pca-wordcloud-jieba-on-harry-potter-series-and-chinese-content-ca6f845b3293"},{"key":"4725_CR61","unstructured":"https:\/\/www.tensorflow.org\/tutorials\/representation\/word2vec"},{"key":"4725_CR62","unstructured":"Zhang Y, Wallace B (2015) A sensitivity analysis of (and practitioners\u2019 guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820"},{"key":"4725_CR63","unstructured":"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC5300751\/"},{"key":"4725_CR64","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.artmed.2018.11.004","volume":"97","author":"I Banerjee","year":"2019","unstructured":"Banerjee I et al (2019) Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artif Intell Med 97:79\u201388","journal-title":"Artif Intell Med"},{"issue":"2\u20133","key":"4725_CR65","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s10791-017-9319-5","volume":"21","author":"X Yang","year":"2018","unstructured":"Yang X, Macdonald C, Ounis I (2018) Using word embeddings in twitter election classification. Inf Retr J 21(2\u20133):183\u2013207","journal-title":"Inf Retr J"},{"issue":"1","key":"4725_CR66","first-page":"1929","volume":"15","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"4725_CR67","unstructured":"https:\/\/arxiv.org\/pdf\/1707.08214.pdf"},{"issue":"6","key":"4725_CR68","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1049\/iet-sen.2018.0006","volume":"12","author":"Y Liu","year":"2018","unstructured":"Liu Y et al (2018) Feature extraction based on information gain and sequential pattern for English question classification. IET Softw 12(6):520\u2013526","journal-title":"IET Softw"},{"issue":"2","key":"4725_CR69","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1142\/S0218488598000094","volume":"6","author":"S Hochreiter","year":"1998","unstructured":"Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzziness Knowl Syst 6(2):107\u2013116","journal-title":"Int J Uncertain Fuzziness Knowl Syst"},{"key":"4725_CR70","doi-asserted-by":"crossref","unstructured":"Zhou H et al (2016) Exploiting syntactic and semantics information for chemical\u2013disease relation extraction.\u00a0Database2016","DOI":"10.1093\/database\/baw048"},{"key":"4725_CR71","unstructured":"https:\/\/github.com\/thtrieu\/qclass_dl\/blob\/master\/ProjectDescription.pdf, https:\/\/github.com\/thtrieu\/qclass_dl\/blob\/master\/ProjectPresentation.pdf"},{"key":"4725_CR72","unstructured":"Gers FA, Schmidhuber J (2000) Recurrent nets that time and count. In: Proceedings of the IEEE-INNS-ENNS international joint conference on neural networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, vol 3, pp 189\u2013194"},{"key":"4725_CR73","unstructured":"http:\/\/karpathy.github.io\/2015\/05\/21\/rnneffectiveness\/"},{"key":"4725_CR74","unstructured":"http:\/\/www.jackdermody.net\/brightwire\/article\/Sequence_to_Sequence_with_LSTM"},{"key":"4725_CR75","doi-asserted-by":"crossref","unstructured":"Alayba AM, Palade V, England M, Iqbal R (2018) A combined CNN and LSTM model for arabic sentiment analysis. In: International cross-domain conference for machine learning and knowledge extraction. Springer, Cham, pp 179\u2013191","DOI":"10.1007\/978-3-319-99740-7_12"},{"key":"4725_CR76","doi-asserted-by":"crossref","unstructured":"Derici C, Celik K, Kutbay E, Ayd\u0131n Y, G\u00fcng\u00f6r T, \u00d6zg\u00fcr A, Kartal G (2015) Question analysis for a closed domain question answering system. In: International conference on intelligent text processing and computational linguistics. Springer, Cham, pp 468\u2013482","DOI":"10.1007\/978-3-319-18117-2_35"},{"key":"4725_CR77","doi-asserted-by":"crossref","unstructured":"D\u00f6nmez \u0130, Adal\u0131 E (2017) Turkish question answering application with course-grained semantic matrix representation of sentences. In: Computer science and engineering (UBMK), 2017 international conference on IEEE, pp 6\u201311","DOI":"10.1109\/UBMK.2017.8093464"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04725-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-020-04725-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04725-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:36:18Z","timestamp":1611102978000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-020-04725-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,21]]},"references-count":77,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["4725"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-04725-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,21]]},"assertion":[{"value":"21 February 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}