{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:20:44Z","timestamp":1778170844666,"version":"3.51.4"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T00:00:00Z","timestamp":1657324800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T00:00:00Z","timestamp":1657324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Inf Syst"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s10844-022-00724-6","type":"journal-article","created":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T11:02:48Z","timestamp":1657364568000},"page":"755-777","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Entity-aware answer sentence selection for question answering with transformer-based language models"],"prefix":"10.1007","volume":"59","author":[{"given":"Zahra","family":"Abbasiantaeb","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8110-1342","authenticated-orcid":false,"given":"Saeedeh","family":"Momtazi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,9]]},"reference":[{"issue":"6","key":"724_CR1","doi-asserted-by":"publisher","first-page":"e1412","DOI":"10.1002\/widm.1412","volume":"11","author":"Z Abbasiantaeb","year":"2021","unstructured":"Abbasiantaeb, Z., & Momtazi, S. (2021). Text-based question answering from information retrieval and deep neural network perspectives: a survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(6), e1412. 10.1002\/widm.1412.","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"key":"724_CR2","doi-asserted-by":"publisher","unstructured":"Bian, W., Li, S., Yang, Z., Chen, G., & Lin, Z. (2017). A compare-aggregate model with dynamic-clip attention for answer selection. In Proceedings of the 2017 ACM on conference on information and knowledge management\u00a0(pp. 1987\u20131990). ACM: CIKM \u201917. https:\/\/doi.org\/10.1145\/3132847.3133089","DOI":"10.1145\/3132847.3133089"},{"key":"724_CR3","doi-asserted-by":"publisher","unstructured":"Cortes, E.G., Woloszyn, V., Barone, D., M\u00f6ller, S, & Vieira, R. (2021). A systematic review of question answering systems for non-factoid questions. Journal of Intelligent Information Systems, 1\u201328. https:\/\/doi.org\/10.1007\/s10844-021-00655-8","DOI":"10.1007\/s10844-021-00655-8"},{"key":"724_CR4","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training Of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies Volume 1 (Long and Short Papers)\u00a0(pp. 4171\u20134186). Minneapolis: Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"issue":"2","key":"724_CR5","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s10844-019-00584-7","volume":"55","author":"E Dimitrakis","year":"2020","unstructured":"Dimitrakis, E., Sgontzos, K., & Tzitzikas, Y. (2020). A survey on question answering systems over linked data and documents. Journal of Intelligent Information Systems, 55(2), 233\u2013259.","journal-title":"Journal of Intelligent Information Systems"},{"key":"724_CR6","doi-asserted-by":"publisher","first-page":"103263","DOI":"10.1109\/ACCESS.2019.2931357","volume":"7","author":"X Feng","year":"2019","unstructured":"Feng, X., & Zeng, Y. (2019). Neural collaborative embedding from reviews for recommendation. IEEE Access, 7, 103263\u2013103274 https:\/\/doi.org\/10.1109\/ACCESS.2019.2931357.","journal-title":"IEEE Access"},{"key":"724_CR7","doi-asserted-by":"publisher","unstructured":"Garg, S., Vu, T., & Moschitti, A. (2020). Tanda: Transfer and adapt pre-trained transformer models for answer sentence selection. In Proceedings of the AAAI conference on artificial intelligence\u00a0(pp. 7780\u20137788). https:\/\/doi.org\/10.1609\/aaai.v34i05.6282","DOI":"10.1609\/aaai.v34i05.6282"},{"key":"724_CR8","doi-asserted-by":"publisher","unstructured":"Guo, J., Fan, Y., Ai, Q., & Croft, WB. (2016). A deep relevance matching model for ad-hoc retrieval. In Proceedings of the 25th ACM international on conference on information and knowledge management\u00a0(pp. 55\u201364). New York: Association for Computing Machinery. https:\/\/doi.org\/10.1145\/2983323.2983769","DOI":"10.1145\/2983323.2983769"},{"key":"724_CR9","doi-asserted-by":"publisher","unstructured":"He, H., & Lin, J. (2016). Pairwise word interaction modeling with deep neural networks for semantic similarity measurement. In Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics, human language technologies\u00a0(pp. 937\u2013948). San Diego: Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/N16-1108","DOI":"10.18653\/v1\/N16-1108"},{"key":"724_CR10","doi-asserted-by":"publisher","unstructured":"Hermjakob, U. (2001). Parsing and question classification for question answering. In Proceedings of the workshop on open-domain question answering\u00a0(Vol. 12 pp. 1\u20136). USA: Association for Computational Linguistics. https:\/\/doi.org\/10.3115\/1117856.1117859","DOI":"10.3115\/1117856.1117859"},{"key":"724_CR11","unstructured":"Hoang, M., Bihorac, OA., & Rouces, J. (2019). Aspect-based sentiment analysis using BERT. In Proceedings of the 22nd nordic conference on computational linguistics\u00a0(pp. 187\u2013196). Turku: Link\u00f6ping University Electronic Press. https:\/\/aclanthology.org\/W19-6120"},{"issue":"12","key":"724_CR12","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.5281\/zenodo.1081840","volume":"2","author":"MR Kangavari","year":"2008","unstructured":"Kangavari, M.R., Ghandchi, S., & Golpour, M. (2008). Information retrieval: Improving question answering systems by query reformulation and answer validation. International Journal of Industrial and Manufacturing Engineering, 2(12), 1275\u20131282. https:\/\/doi.org\/10.5281\/zenodo.1081840.","journal-title":"International Journal of Industrial and Manufacturing Engineering"},{"key":"724_CR13","doi-asserted-by":"publisher","unstructured":"Karimi, A., Rossi, L., & Prati, A. (2021). Adversarial training for aspect-based sentiment analysis with bert. In 2020 25Th international conference on pattern recognition\u00a0(pp. 8797\u20138803). ICPR: IEEE Computer Society. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412167","DOI":"10.1109\/ICPR48806.2021.9412167"},{"key":"724_CR14","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1162\/tacl_a_00276","volume":"7","author":"T Kwiatkowski","year":"2019","unstructured":"Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., Toutanova, K., Jones, L., Kelcey, M., Chang, MW., Dai, A., Uszkoreit, J., Le, Q., & Petrov, S. (2019). Natural questions: A benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7, 453\u2013466. https:\/\/doi.org\/10.1162\/tacl_a_00276.","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"724_CR15","doi-asserted-by":"publisher","unstructured":"Li, W., Gao, S., Zhou, H., Huang, Z., Zhang, K., & Li, W. (2019). The automatic text classification method based on bert and feature union, IEEE, ICPADS. https:\/\/doi.org\/10.1109\/ICPADS47876.2019.00114.","DOI":"10.1109\/ICPADS47876.2019.00114"},{"key":"724_CR16","doi-asserted-by":"publisher","unstructured":"Li, X., & Roth, D. (2002a). Learning question classifiers. In Proceedings of the 19th international conference on computational linguistics\u00a0(Vol. 1 pp. 1\u20137). USA: Association for Computational Linguistics. https:\/\/doi.org\/10.3115\/1072228.1072378","DOI":"10.3115\/1072228.1072378"},{"key":"724_CR17","doi-asserted-by":"publisher","unstructured":"Li, X., & Roth, D. (2002b). Learning question classifiers. In Proceedings of the 19th international conference on computational linguistics\u00a0(Vol. 1 pp. 1\u20137). USA: Association for Computational Linguistics. https:\/\/doi.org\/10.3115\/1072228.1072378","DOI":"10.3115\/1072228.1072378"},{"issue":"6","key":"724_CR18","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1049\/iet-sen.2018.0006","volume":"12","author":"Y Liu","year":"2018","unstructured":"Liu, Y., Yi, X., Chen, R., Zhai, Z., & Gu, J. (2018). Feature extraction based on information gain and sequential pattern for english question classification. IET Software, 12(6), 520\u2013526. https:\/\/doi.org\/10.1049\/iet-sen.2018.0006.","journal-title":"IET Software"},{"key":"724_CR19","doi-asserted-by":"publisher","first-page":"12809","DOI":"10.1109\/ACCESS.2019.2893355","volume":"7","author":"J Lv","year":"2019","unstructured":"Lv, J., Song, B., Guo, J., Du, X., & Guizani, M. (2019). Interest-related item similarity model based on multimodal data for top-n recommendation. IEEE Access, 7, 12809\u201312821. https:\/\/doi.org\/10.1109\/ACCESS.2019.2893355.","journal-title":"IEEE Access"},{"key":"724_CR20","doi-asserted-by":"publisher","unstructured":"Momtazi, S., & Klakow, D. (2011). Trained trigger language model for sentence retrieval in qa: Bridging the vocabulary gap. In Proceedings of the 20th ACM international conference on information and knowledge management\u00a0(pp. 2005\u20132008). New York: Association for Computing Machinery. https:\/\/doi.org\/10.1145\/2063576.2063876","DOI":"10.1145\/2063576.2063876"},{"key":"724_CR21","unstructured":"Mozafari, J., Fatemi, A., & Nematbakhsh, MA. (2019). Bas: An answer selection method using bert language model. arXiv:1911.01528."},{"key":"724_CR22","doi-asserted-by":"publisher","unstructured":"Peters, M.E., Ammar, W., Bhagavatula, C., & Power, R. (2017). Semi-supervised sequence tagging with bidirectional language models. In Proceedings of the 55th annual meeting of the association for computational linguistics Volume 1 (Long Papers)\u00a0(pp. 1756\u20131765). Canada: Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/P17-1161","DOI":"10.18653\/v1\/P17-1161"},{"issue":"14","key":"724_CR23","doi-asserted-by":"publisher","first-page":"4710","DOI":"10.3390\/app10144710","volume":"10","author":"M Pota","year":"2020","unstructured":"Pota, M., Esposito, M., De Pietro, G., & Fujita, H. (2020). Best practices of convolutional neural networks for question classification. Applied Sciences, 10(14), 4710\u20134723. https:\/\/doi.org\/10.3390\/app10144710.","journal-title":"Applied Sciences"},{"key":"724_CR24","unstructured":"Radford, A. (2018). Improving language understanding by generative pre-training."},{"key":"724_CR25","doi-asserted-by":"publisher","unstructured":"Ravichandran, D., & Hovy, E. (2002). Learning surface text patterns for a question answering system. In Proceedings of the 40th annual meeting on association for computational linguistics\u00a0(pp. 41\u201347). USA: Association for Computational Linguistics. https:\/\/doi.org\/10.3115\/1073083.1073092","DOI":"10.3115\/1073083.1073092"},{"key":"724_CR26","unstructured":"Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv:191001108."},{"key":"724_CR27","unstructured":"Seidakhmetov, T. (2020). Question type classification methods comparison. arXiv:200100571."},{"key":"724_CR28","doi-asserted-by":"publisher","unstructured":"Severyn, A., & Moschitti, A. (2015). Learning to rank short text pairs with convolutional deep neural networks. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval\u00a0(pp. 373\u2013382). New York: Association for Computing Machinery. https:\/\/doi.org\/10.1145\/2766462.2767738","DOI":"10.1145\/2766462.2767738"},{"key":"724_CR29","doi-asserted-by":"publisher","unstructured":"Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., & Jiang, P. (2019). Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management\u00a0(pp. 1441\u20131450). New York: Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3357384.3357895","DOI":"10.1145\/3357384.3357895"},{"key":"724_CR30","doi-asserted-by":"publisher","unstructured":"Tan, M., dos Santos, C., Xiang, B., & Zhou, B. (2016). Improved representation learning for question answer matching. In Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1 Long Papers)\u00a0(pp. 464\u2013473). Germany: Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/P16-1044","DOI":"10.18653\/v1\/P16-1044"},{"key":"724_CR31","doi-asserted-by":"publisher","unstructured":"Tay, Y., Phan, MC., Tuan, LA., & Hui, SC. (2017). Learning to rank question answer pairs with holographic dual lstm architecture. In Proceedings of the 40th International ACM SIGIR conference on research and development in information retrieval\u00a0(pp. 695\u2013704). New York: ACM. https:\/\/doi.org\/10.1145\/3077136.3080790","DOI":"10.1145\/3077136.3080790"},{"key":"724_CR32","doi-asserted-by":"publisher","unstructured":"Tay, Y., Tuan, LA., & Hui, SC. (2018). Multi-cast attention networks. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining\u00a0(pp. 2299\u20132308). New York: Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3219819.3220048","DOI":"10.1145\/3219819.3220048"},{"key":"724_CR33","doi-asserted-by":"publisher","unstructured":"Wan, S., Lan, Y., Xu, J., Guo, J., Pang, L., & Cheng, X. (2016). Match-srnn: Modeling the recursive matching structure with spatial rnn. In Proceedings of the twenty-fifth international joint conference on artificial intelligence\u00a0(pp. 2922\u20132928). AAAI Press. https:\/\/doi.org\/10.5555\/3060832.3061030","DOI":"10.5555\/3060832.3061030"},{"key":"724_CR34","doi-asserted-by":"publisher","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018). GLUE: A multi-task benchmark and analysis platform for natural language understanding. In Proceedings of the 2018 EMNLP workshop BlackboxNLP analyzing and interpreting neural networks for NLP\u00a0(pp. 353\u2013355). Belgium: Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/W18-5446","DOI":"10.18653\/v1\/W18-5446"},{"key":"724_CR35","doi-asserted-by":"publisher","unstructured":"Wang, B., Liu, K., & Zhao, J. (2016). Inner attention based recurrent neural networks for answer selection. In Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1 Long Papers)\u00a0(pp. 1288\u20131297). Germany: Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/P16-1122","DOI":"10.18653\/v1\/P16-1122"},{"key":"724_CR36","doi-asserted-by":"publisher","unstructured":"Wang, D., & Nyberg, E. (2015). A long short-term memory model for answer sentence selection in question answering. In Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (Volume 2 Short Papers)\u00a0(pp. 707\u2013712). China: Association for Computational Linguistics. https:\/\/doi.org\/10.3115\/v1\/P15-2116","DOI":"10.3115\/v1\/P15-2116"},{"key":"724_CR37","unstructured":"Wang, M., Smith, NA., & Mitamura, T. (2007). What is the Jeopardy model? a quasi-synchronous grammar for QA. In Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL)\u00a0(pp. 22\u201332). Czech Republic: Association for Computational Linguistics. https:\/\/aclanthology.org\/D07-1003"},{"key":"724_CR38","unstructured":"Wang, S., & Jiang, J. (2017). A compare-aggregate model for matching text sequences. In Proceedings of the 5th international conference on learning representations, international conference on learning representations (ICLR)."},{"key":"724_CR39","doi-asserted-by":"publisher","unstructured":"Wang, Z., Hamza, W., & Florian, R. (2017). Bilateral multi-perspective matching for natural language sentences. In Proceedings of the 26th international joint conference on artificial intelligence\u00a0(pp. 4144\u20134150). AAAI Press. https:\/\/doi.org\/10.24963\/ijcai.2017\/579","DOI":"10.24963\/ijcai.2017\/579"},{"key":"724_CR40","unstructured":"Weischedel, R., Palmer, M., Marcus, M., Hovy, E., Pradhan, S., Ramshaw, L., Xue, N., Taylor, A., Kaufman, J., Franchini, M., & et al. (2013). Ontonotes release 5.0 ldc2013t19. Linguistic Data Consortium, Philadelphia, PA, 23."},{"key":"724_CR41","doi-asserted-by":"publisher","unstructured":"Yang, L., Ai, Q., Guo, J., & Croft, WB. (2016). anmm: Ranking short answer texts with attention-based neural matching model. In Proceedings of the 25th ACM international on conference on information and knowledge management\u00a0(pp. 287\u2013296). New York: ACM. https:\/\/doi.org\/10.1145\/2983323.2983818","DOI":"10.1145\/2983323.2983818"},{"key":"724_CR42","doi-asserted-by":"publisher","unstructured":"Yang, R., Zhang, J., Gao, X., Ji, F., & Chen, H. (2019). Simple and effective text matching with richer alignment features. In Proceedings of the 57th annual meeting of the association for computational linguistics\u00a0(pp. 4699\u20134709). Italy: Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/P19-1465","DOI":"10.18653\/v1\/P19-1465"},{"key":"724_CR43","doi-asserted-by":"publisher","unstructured":"Yang, Y., Yih, W.T., & Meek, C. (2015). WikiQA: A challenge dataset for open-domain question answering. In Proceedings of the 2015 conference on empirical methods in natural language processing\u00a0(pp. 2013\u20132018). Portugal: Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/D15-1237","DOI":"10.18653\/v1\/D15-1237"},{"key":"724_CR44","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1162\/tacl_a_00097","volume":"4","author":"W Yin","year":"2016","unstructured":"Yin, W., Sch\u00fctze, H., Xiang, B., & Zhou, B. (2016). Abcnn: Attention-based convolutional neural network for modeling sentence pairs. Transactions of the Association for Computational Linguistics, 4, 259\u2013272. https:\/\/doi.org\/10.1162\/tacl_a_00097.","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"724_CR45","doi-asserted-by":"publisher","unstructured":"Yoon, S., Dernoncourt, F., Kim, D.S., Bui, T., & Jung, K. (2019). A compare-aggregate model with latent clustering for answer selection. In Proceedings of the 28th ACM international conference on information and knowledge management\u00a0(pp. 2093\u20132096). CIKM: Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3357384.3358148","DOI":"10.1145\/3357384.3358148"},{"key":"724_CR46","unstructured":"Yu, L., Hermann, K.M., Blunsom, P., & Pulman, S.G. (1632). Deep learning for answer sentence selection. In Deep learning and representation learning workshop: NIPS 2014, vol abs\/1412."},{"key":"724_CR47","doi-asserted-by":"publisher","first-page":"176600","DOI":"10.1109\/ACCESS.2019.2953990","volume":"7","author":"S Yu","year":"2019","unstructured":"Yu, S., Su, J., & Luo, D. (2019). Improving bert-based text classification with auxiliary sentence and domain knowledge. IEEE Access, 7, 176600\u2013176612. https:\/\/doi.org\/10.1109\/ACCESS.2019.2953990.","journal-title":"IEEE Access"},{"key":"724_CR48","doi-asserted-by":"publisher","unstructured":"Zhang, D., & Lee, WS. (2003). Question classification using support vector machines. In Proceedings of the 26th annual international ACM SIGIR conference on research and development in informaion retrieval\u00a0(pp. 26\u201332). New York: Association for Computing Machinery. https:\/\/doi.org\/10.1145\/860435.860443","DOI":"10.1145\/860435.860443"},{"key":"724_CR49","doi-asserted-by":"publisher","unstructured":"Zheng, S., & Yang, M. (2019). A new method of improving bert for text classification. In Z. Cui, J. Pan, S. Zhang, L. Xiao, & J. Yang (Eds.) Intelligence science and big data engineering. big data and machine learning\u00a0(pp. 442\u2013452). Cham: Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-36204-1_37","DOI":"10.1007\/978-3-030-36204-1_37"},{"key":"724_CR50","unstructured":"Zhuang, L., Wayne, L., Ya, S., & Jun, Z. (2021). A robustly optimized BERT pre-training approach with post-training. In Proceedings of the 20th chinese national conference on computational linguistics (pp. 1218\u20131227). China: Chinese Information Processing Society of China."}],"container-title":["Journal of Intelligent Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-022-00724-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10844-022-00724-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-022-00724-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T02:11:14Z","timestamp":1668737474000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10844-022-00724-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,9]]},"references-count":50,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["724"],"URL":"https:\/\/doi.org\/10.1007\/s10844-022-00724-6","relation":{},"ISSN":["0925-9902","1573-7675"],"issn-type":[{"value":"0925-9902","type":"print"},{"value":"1573-7675","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,9]]},"assertion":[{"value":"17 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2022","order":4,"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":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}