{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T21:04:40Z","timestamp":1775077480128,"version":"3.50.1"},"reference-count":152,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T00:00:00Z","timestamp":1600905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T00:00:00Z","timestamp":1600905600000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s00521-020-05321-8","type":"journal-article","created":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T14:02:49Z","timestamp":1600956169000},"page":"5437-5469","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3062-9996","authenticated-orcid":false,"given":"Muhammad Nabeel","family":"Asim","sequence":"first","affiliation":[]},{"given":"Muhammad Usman","family":"Ghani","sequence":"additional","affiliation":[]},{"given":"Muhammad Ali","family":"Ibrahim","sequence":"additional","affiliation":[]},{"given":"Waqar","family":"Mahmood","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Dengel","sequence":"additional","affiliation":[]},{"given":"Sheraz","family":"Ahmed","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,24]]},"reference":[{"key":"5321_CR1","unstructured":"Mulcahy M (2017) Big data statistics and facts for 2017. https:\/\/www.waterfordtechnologies.com\/big-data-interesting-facts\/. [Online; Accessed 1 Jan 2018]"},{"key":"5321_CR2","unstructured":"Cave A (2017) What will we do when the world\u2019s data hits 163 Zettabytes in 2025. https:\/\/www.forbes.com\/sites\/andrewcave\/2017\/04\/13\/what-will-we-do-when-the-worlds-data-hits-163-zettabytes-in-2025\/#612b04f8349a\/. [Online; Accessed 1 Jan 2018]"},{"key":"5321_CR3","unstructured":"Marr B (2015) Big data: 20 mind-boggling facts everyone must Read. https:\/\/www.forbes.com\/sites\/bernardmarr\/2015\/09\/30\/big-data-20-mind-boggling-facts-everyone-must-read\/#301b174517b1\/. [Online; Accessed 1 Jan 2018]"},{"key":"5321_CR4","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.engappai.2014.11.001","volume":"39","author":"I Idris","year":"2015","unstructured":"Idris I, Selamat A, Nguyen NT, Omatu S, Krejcar O, Kuca K, Penhaker M (2015) A combined negative selection algorithm\u2013particle swarm optimization for an email spam detection system. Eng Appl Artif Intell 39:33\u201344","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"5321_CR5","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.diin.2011.04.002","volume":"8","author":"N Cheng","year":"2011","unstructured":"Cheng N, Chandramouli R, Subbalakshmi KP (2011) Author gender identification from text. Digit Investig 8(1):78\u201388","journal-title":"Digit Investig"},{"issue":"6","key":"5321_CR6","first-page":"5107","volume":"6","author":"A Bhatt","year":"2015","unstructured":"Bhatt A, Patel A, Chheda H, Gawande K (2015) Amazon review classification and sentiment analysis. Int J Comput Sci Inf Technol 6(6):5107\u20135110","journal-title":"Int J Comput Sci Inf Technol"},{"key":"5321_CR7","doi-asserted-by":"crossref","unstructured":"Dilrukshi I, De\u00a0Zoysa K, Caldera A (2013) Twitter news classification using svm. In: 2013 8th International conference on computer science & Education (ICCSE), pp 287\u2013291. IEEE","DOI":"10.1109\/ICCSE.2013.6553926"},{"issue":"19","key":"5321_CR8","first-page":"98","volume":"1","author":"G Krishnalal","year":"2010","unstructured":"Krishnalal G, Babu RS, Srinivasagan KG (2010) A new text mining approach based on hmm-svm for web news classification. Int J Comput Appl 1(19):98\u2013104","journal-title":"Int J Comput Appl"},{"key":"5321_CR9","unstructured":"Kroha P, Baeza-Yates R (2005) A case study: news classification based on term frequency. In: Sixteenth international workshop on database and expert systems applications, 2005. Proceedings. pp 428\u2013432. IEEE"},{"issue":"1","key":"5321_CR10","first-page":"817","volume":"4","author":"M Gahirwal","year":"2018","unstructured":"Gahirwal M, Moghe S, Kulkarni T, Khakhar D, Bhatia J (2018) Fake news detection. Int J Adv Res Ideas Innov Technol 4(1):817\u2013819","journal-title":"Int J Adv Res Ideas Innov Technol"},{"key":"5321_CR11","unstructured":"Conroy Niall\u00a0J, Rubin Victoria\u00a0L, Chen Y (2015) Automatic deception detection: methods for finding fake news. In: Proceedings of the 78th ASIS&T annual meeting: information science with impact: research in and for the community, pp 82. American Society for Information Science"},{"issue":"1","key":"5321_CR12","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/3137597.3137600","volume":"19","author":"K Shu","year":"2017","unstructured":"Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter 19(1):22\u201336","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"5321_CR13","doi-asserted-by":"crossref","unstructured":"Akram Q, Naseer A, Hussain S (2009) Assas-band, an affix-exception-list based urdu stemmer. In: Proceedings of the 7th workshop on Asian language resources, pp 40\u201346. Association for Computational Linguistics","DOI":"10.3115\/1690299.1690305"},{"key":"5321_CR14","doi-asserted-by":"crossref","unstructured":"Ali AR, Ijaz M (2009) Urdu text classification. In: Proceedings of the 7th international conference on frontiers of information technology, pp 21. ACM","DOI":"10.1145\/1838002.1838025"},{"issue":"8","key":"5321_CR15","first-page":"265","volume":"7","author":"M Usman","year":"2016","unstructured":"Usman M, Shafique Z, Ayub S, Malik K (2016) Urdu text classification using majority voting. Int J Adv Comput Sci Appl 7(8):265\u2013273","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"1","key":"5321_CR16","doi-asserted-by":"crossref","first-page":"17","DOI":"10.4316\/JACSM.201601002","volume":"10","author":"K Ahmed","year":"2016","unstructured":"Ahmed K, Ali M, Khalid S, Kamran M (2016) Framework for urdu news headlines classification. J Appl Comput Sci Math 10(1):17\u201321","journal-title":"J Appl Comput Sci Math"},{"issue":"29","key":"5321_CR17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17485\/ijst\/2017\/v10i29\/116525","volume":"10","author":"SA Sattar","year":"2017","unstructured":"Sattar SA, Hina S, Khursheed N, Hamid A (2017) Urdu documents classification using na\u00efve bayes. Indian J Sci Technol 10(29):1\u20134","journal-title":"Indian J Sci Technol"},{"issue":"6","key":"5321_CR18","first-page":"33","volume":"7","author":"Z Tehseen","year":"2015","unstructured":"Tehseen Z, Qaiser A, Muhammad Pervez A (2015) Evaluation of feature selection approaches for urdu text categorization. Int J Intell Syst Appl 7(6):33","journal-title":"Int J Intell Syst Appl"},{"key":"5321_CR19","unstructured":"Hussain S, Adeeba F, Akram Q (2016) Urdu text genre identification. In: Proceedings of conference on language and technology, 2016 (CLT 16), Lahore, Pakistan. CLE,"},{"key":"5321_CR20","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.neucom.2015.01.070","volume":"159","author":"G Chen","year":"2015","unstructured":"Chen G, Chen J (2015) A novel wrapper method for feature selection and its applications. Neurocomputing 159:219\u2013226","journal-title":"Neurocomputing"},{"key":"5321_CR21","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.eswa.2018.07.028","volume":"114","author":"A Rehman","year":"2018","unstructured":"Rehman A, Javed K, Babri HA, Asim N (2018) Selection of the most relevant terms based on a max\u2013min ratio metric for text classification. Expert Syst Appl 114:78\u201396","journal-title":"Expert Syst Appl"},{"key":"5321_CR22","doi-asserted-by":"crossref","unstructured":"Parlak B, Uysal AK (2016) The impact of feature selection on medical document classification. In: 2016 11th Iberian conference on information systems and technologies (CISTI), pp 1\u20135. IEEE","DOI":"10.1109\/CISTI.2016.7521524"},{"key":"5321_CR23","unstructured":"Prusa JD, Khoshgoftaar TM, Dittman DJ (2015) Impact of feature selection techniques for tweet sentiment classification. In: The Twenty-eighth international flairs conference"},{"issue":"1","key":"5321_CR24","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ipm.2013.08.006","volume":"50","author":"Alper Kursat Uysal and Serkan Gunal","year":"2014","unstructured":"Alper Kursat Uysal and Serkan Gunal (2014) The impact of preprocessing on text classification. Inf Process Manag 50(1):104\u2013112","journal-title":"Inf Process Manag"},{"key":"5321_CR25","unstructured":"Weston J, Watkins C (1998) Multi-class support vector machines. Technical report, Citeseer"},{"issue":"2\u20133","key":"5321_CR26","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1023\/A:1007413511361","volume":"29","author":"P Domingos","year":"1997","unstructured":"Domingos P, Pazzani M (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29(2\u20133):103\u2013130","journal-title":"Mach Learn"},{"key":"5321_CR27","doi-asserted-by":"crossref","unstructured":"Klenin J, Botov D (2017) Comparison of vector space representations of documents for the task of matching contents of educational course programmes. In: AIST (Supplement), pp 79\u201390","DOI":"10.1007\/978-3-319-71746-3_14"},{"key":"5321_CR28","unstructured":"Li H, Caragea D, Li X, Caragea C (2018) Comparison of word embeddings and sentence encodings as generalized representations for crisis tweet classification tasks. en. In: New Zealand, pp 13"},{"key":"5321_CR29","unstructured":"Almeida F, Xex\u00e9o G (2019) Word embeddings: a survey. arXiv preprint arXiv:1901.09069"},{"issue":"11","key":"5321_CR30","doi-asserted-by":"crossref","first-page":"113","DOI":"10.3390\/fi10110113","volume":"10","author":"Y Li","year":"2018","unstructured":"Li Y, Wang X, Pengjian X (2018) Chinese text classification model based on deep learning. Future Internet 10(11):113","journal-title":"Future Internet"},{"key":"5321_CR31","doi-asserted-by":"crossref","unstructured":"Kamath CN, Bukhari SS, Dengel A (2018) Comparative study between traditional machine learning and deep learning approaches for text classification. In: Proceedings of the ACM symposium on document engineering 2018, pp 14. ACM","DOI":"10.1145\/3209280.3209526"},{"issue":"294","key":"5321_CR32","first-page":"296","volume":"390","author":"JJ Rubio","year":"2020","unstructured":"Rubio JJ, Pan Y, Lughofer E, Chen M-Y, Qiu J (2020) Fast learning of neural networks with application to big data processes. Neurocomputing 390:294\u2013296","journal-title":"Neurocomputing"},{"issue":"6","key":"5321_CR33","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1109\/TFUZZ.2009.2029569","volume":"17","author":"Jos\u00e9 de Jes\u00fas Rubio","year":"2009","unstructured":"Jos\u00e9 de Jes\u00fas Rubio (2009) Sofmls: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296\u20131309","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"5321_CR34","doi-asserted-by":"crossref","first-page":"31968","DOI":"10.1109\/ACCESS.2018.2846483","volume":"6","author":"Jes\u00fas Alberto Meda-Campa\u00f1a","year":"2018","unstructured":"Jes\u00fas Alberto Meda-Campa\u00f1a (2018) On the estimation and control of nonlinear systems with parametric uncertainties and noisy outputs. IEEE Access 6:31968\u201331973","journal-title":"IEEE Access"},{"issue":"5","key":"5321_CR35","doi-asserted-by":"crossref","first-page":"6731","DOI":"10.3233\/JIFS-190216","volume":"37","author":"J de Jos\u00e9 Rubio","year":"2019","unstructured":"de Jos\u00e9 Rubio J, Enrique G, Genaro O, Israel E, David Ricardo C, Ricardo B, Jesus L, Juan Francisco N (2019) Unscented kalman filter for learning of a solar dryer and a greenhouse. J Intell Fuzzy Syst 37(5):6731\u20136741","journal-title":"J Intell Fuzzy Syst"},{"key":"5321_CR36","unstructured":"Haider S (2018) Urdu word embeddings. In: Proceedings of the eleventh international conference on language resources and evaluation (LREC-2018)"},{"key":"5321_CR37","unstructured":"Yang W, Lu W, Zheng VW (2019) A simple regularization-based algorithm for learning cross-domain word embeddings. arXiv preprint arXiv:1902.00184"},{"key":"5321_CR38","unstructured":"You S, Ding D, Canini K, Pfeifer J, Gupta M (2017) Deep lattice networks and partial monotonic functions. In: Advances in neural information processing systems, pp 2981\u20132989"},{"key":"5321_CR39","unstructured":"Niebler T, Becker M, P\u00f6litz C, Hotho A (2017) Learning semantic relatedness from human feedback using metric learning. arXiv preprint arXiv:1705.07425"},{"key":"5321_CR40","doi-asserted-by":"crossref","unstructured":"Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. arXiv preprint arXiv:1802.05365","DOI":"10.18653\/v1\/N18-1202"},{"key":"5321_CR41","doi-asserted-by":"crossref","unstructured":"Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146","DOI":"10.18653\/v1\/P18-1031"},{"key":"5321_CR42","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"5321_CR43","unstructured":"You Y, Li J, Hseu J, Song X, Demmel J, Hsieh CJ (2019) Reducing bert pre-training time from 3 days to 76 minutes. arXiv preprint arXiv:1904.00962"},{"key":"5321_CR44","unstructured":"You Y, Li J, Reddi S, Hseu J, Kumar S, Bhojanapalli S, Song X, Demmel J, Hsieh CJ (2019) Large batch optimization for deep learning: training bert in 76 minutes. arXiv preprint arXiv:1904.00962, 1(5)"},{"key":"5321_CR45","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V(2019) Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692"},{"key":"5321_CR46","doi-asserted-by":"crossref","unstructured":"Asim MN, Khan MUG, Malik MI, Dengel A, Ahmed S (2019) A robust hybrid approach for textual document classification. In: 2019 International conference on document analysis and recognition (ICDAR), pp 1390\u20131396. IEEE","DOI":"10.1109\/ICDAR.2019.00224"},{"issue":"2","key":"5321_CR47","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.ipm.2016.12.004","volume":"53","author":"R Abdur","year":"2017","unstructured":"Abdur R, Javid K, Babri HA (2017) Feature selection based on a normalized difference measure for text classification. Inf Process Manag 53(2):473\u2013489","journal-title":"Inf Process Manag"},{"key":"5321_CR48","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1613\/jair.514","volume":"11","author":"P Resnik","year":"1999","unstructured":"Resnik P (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res 11:95\u2013130","journal-title":"J Artif Intell Res"},{"key":"5321_CR49","first-page":"117","volume":"13","author":"M Thangaraj","year":"2018","unstructured":"Thangaraj M, Sivakami M (2018) Text classification techniques: a literature review. Interdiscip J Inf Knowl Manag 13:117\u2013135","journal-title":"Interdiscip J Inf Knowl Manag"},{"key":"5321_CR50","doi-asserted-by":"crossref","unstructured":"Agarwal B, Mittal N (2014) Text classification using machine learning methods-a survey. In: Proceedings of the second international conference on soft computing for problem solving (SocProS 2012), December 28-30, 2012, pp 701\u2013709. Springer","DOI":"10.1007\/978-81-322-1602-5_75"},{"key":"5321_CR51","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","volume":"300","author":"J Cai","year":"2018","unstructured":"Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70\u201379","journal-title":"Neurocomputing"},{"key":"5321_CR52","doi-asserted-by":"crossref","unstructured":"Asim MN, Wasim M, Ali MS, Rehman A (2017) Comparison of feature selection methods in text classification on highly skewed datasets. In: First international conference on latest trends in electrical engineering and computing technologies (INTELLECT), 2017 , pp 1\u20138. IEEE","DOI":"10.1109\/INTELLECT.2017.8277634"},{"issue":"5","key":"5321_CR53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3234150","volume":"51","author":"S Pouyanfar","year":"2018","unstructured":"Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP, Shyu M-L, Chen S-C, Iyengar SS (2018) A survey on deep learning: algorithms, techniques, and applications. ACM Comput Surveys (CSUR) 51(5):1\u20133","journal-title":"ACM Comput Surveys (CSUR)"},{"key":"5321_CR54","doi-asserted-by":"crossref","unstructured":"Ruder S, Peters ME, Swayamdipta S, Wolf T (2019) Transfer learning in natural language processing. In: Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: tutorials, pp 15\u201318","DOI":"10.18653\/v1\/N19-5004"},{"key":"5321_CR55","first-page":"1289","volume":"3","author":"G Forman","year":"2003","unstructured":"Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3:1289\u20131305","journal-title":"J Mach Learn Res"},{"issue":"7","key":"5321_CR56","doi-asserted-by":"crossref","first-page":"3670","DOI":"10.1016\/j.eswa.2014.12.013","volume":"42","author":"A Rehman","year":"2015","unstructured":"Rehman A, Javed K, Babri HA, Saeed M (2015) Relative discrimination criterion-a novel feature ranking method for text data. Expert Syst Appl 42(7):3670\u20133681","journal-title":"Expert Syst Appl"},{"issue":"1","key":"5321_CR57","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1093\/biomet\/70.1.163","volume":"70","author":"JT Kent","year":"1983","unstructured":"Kent JT (1983) Information gain and a general measure of correlation. Biometrika 70(1):163\u2013173","journal-title":"Biometrika"},{"issue":"3","key":"5321_CR58","doi-asserted-by":"crossref","first-page":"5432","DOI":"10.1016\/j.eswa.2008.06.054","volume":"36","author":"J Chen","year":"2009","unstructured":"Chen J, Huang H, Tian S, Youli Q (2009) Feature selection for text classification with na\u00efve bayes. Expert Syst Appl 36(3):5432\u20135435","journal-title":"Expert Syst Appl"},{"key":"5321_CR59","unstructured":"Park H, Kwon S, Kwon HC (2010) Complete gini-index text (git) feature-selection algorithm for text classification. In: The 2nd international conference on software engineering and data mining, pp 366\u2013371. IEEE"},{"key":"5321_CR60","doi-asserted-by":"crossref","unstructured":"Gao Y, Wang HL (2009) A feature selection algorithm based on poisson estimates. In: 2009 Sixth international conference on fuzzy systems and knowledge discovery, volume\u00a01, pp 13\u201318. IEEE","DOI":"10.1109\/FSKD.2009.712"},{"issue":"2","key":"5321_CR61","first-page":"85","volume":"3","author":"V Korde","year":"2012","unstructured":"Korde V, Mahender CN (2012) Text classification and classifiers: a survey. Int J Artif Intell Appl 3(2):85","journal-title":"Int J Artif Intell Appl"},{"key":"5321_CR62","doi-asserted-by":"crossref","unstructured":"Zhang T (2004) Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Proceedings of the twenty-first international conference on Machine learning, p. 116. ACM","DOI":"10.1145\/1015330.1015332"},{"key":"5321_CR63","unstructured":"McCallum A, Nigam K et\u00a0al (1998) A comparison of event models for naive bayes text classification. In: AAAI-98 workshop on learning for text categorization, volume 752, pp 41\u201348. Citeseer"},{"issue":"12","key":"5321_CR64","first-page":"2913","volume":"7","author":"X Baoxun","year":"2012","unstructured":"Baoxun X, Guo X, Ye Y, Cheng J (2012) An improved random forest classifier for text categorization. JCP 7(12):2913\u20132920","journal-title":"JCP"},{"issue":"2","key":"5321_CR65","first-page":"271","volume":"2","author":"AG Karegowda","year":"2010","unstructured":"Karegowda AG, Manjunath AS, Jayaram MA (2010) Comparative study of attribute selection using gain ratio and correlation based feature selection. Int J Inf Technol Knowl Manag 2(2):271\u2013277","journal-title":"Int J Inf Technol Knowl Manag"},{"key":"5321_CR66","unstructured":"Yu L, Liu H (2003) Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the 20th international conference on machine learning (ICML-03), pp 856\u2013863"},{"issue":"4","key":"5321_CR67","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1016\/j.eswa.2004.12.023","volume":"28","author":"S Tan","year":"2005","unstructured":"Tan S (2005) Neighbor-weighted k-nearest neighbor for unbalanced text corpus. Expert Syst Appl 28(4):667\u2013671","journal-title":"Expert Syst Appl"},{"key":"5321_CR68","unstructured":"Lopez MM, Kalita J (2017) Deep learning applied to nlp. arXiv preprint arXiv:1703.03091"},{"key":"5321_CR69","doi-asserted-by":"crossref","unstructured":"Conneau A, Schwenk H, Barrault L, Lecun Y (2016) Very deep convolutional networks for natural language processing. arXiv preprint arXiv:1606.01781, 2","DOI":"10.18653\/v1\/E17-1104"},{"key":"5321_CR70","doi-asserted-by":"crossref","unstructured":"Camacho-Collados J, Pilehvar MT (2017) On the role of text preprocessing in neural network architectures: an evaluation study on text categorization and sentiment analysis. arXiv preprint arXiv:1707.01780","DOI":"10.18653\/v1\/W18-5406"},{"issue":"2","key":"5321_CR71","doi-asserted-by":"crossref","first-page":"27","DOI":"10.3390\/a9020027","volume":"9","author":"A Ayedh","year":"2016","unstructured":"Ayedh A, Tan G, Alwesabi K, Rajeh H (2016) The effect of preprocessing on arabic document categorization. Algorithms 9(2):27","journal-title":"Algorithms"},{"key":"5321_CR72","doi-asserted-by":"crossref","unstructured":"Malaviya C, Wu S, Cotterell R (2019) A simple joint model for improved contextual neural lemmatization. arXiv preprint arXiv:1904.02306","DOI":"10.18653\/v1\/N19-1155"},{"key":"5321_CR73","doi-asserted-by":"crossref","unstructured":"Yulia L (2008) Effect of preprocessing on extractive summarization with maximal frequent sequences. In: Mexican international conference on artificial intelligence, pp 123\u2013132. Springer, 2008","DOI":"10.1007\/978-3-540-88636-5_11"},{"key":"5321_CR74","unstructured":"Danisman T, Alpkocak A (2008) Feeler: emotion classification of text using vector space model. In: AISB 2008 convention communication, interaction and social intelligence, volume\u00a01, pp 53"},{"issue":"3","key":"5321_CR75","first-page":"7","volume":"4","author":"D Sharma","year":"2012","unstructured":"Sharma D, Cse M (2012) Stemming algorithms: a comparative study and their analysis. Int J Appl Inf Syst 4(3):7\u201312","journal-title":"Int J Appl Inf Syst"},{"issue":"150\u2013164","key":"5321_CR76","first-page":"10","volume":"9","author":"V Kanhirangat","year":"2016","unstructured":"Kanhirangat V, Gupta D (2016) A study on extrinsic text plagiarism detection techniques and tools. J Eng Sci Technol Rev 9(150\u2013164):10","journal-title":"J Eng Sci Technol Rev"},{"issue":"1","key":"5321_CR77","first-page":"1","volume":"7","author":"K Latha","year":"2010","unstructured":"Latha K (2010) A dynamic feature selection method for document ranking with relevance feedback approach. ICTACT J Soft Comput 7(1):1\u20138","journal-title":"ICTACT J Soft Comput"},{"issue":"1\u20132","key":"5321_CR78","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","volume":"97","author":"R Kohavi","year":"1997","unstructured":"Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1\u20132):273\u2013324","journal-title":"Artif Intell"},{"key":"5321_CR79","doi-asserted-by":"crossref","unstructured":"Lal TN, Chapelle O, Weston J, Elisseeff A (2006) Embedded methods. In: Feature extraction, pp 137\u2013165. Springer","DOI":"10.1007\/978-3-540-35488-8_6"},{"key":"5321_CR80","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157\u20131182","journal-title":"J Mach Learn Res"},{"issue":"1\u20134","key":"5321_CR81","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3233\/IDA-1997-1302","volume":"1","author":"M Dash","year":"1997","unstructured":"Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(1\u20134):131\u2013156","journal-title":"Intell Data Anal"},{"issue":"5","key":"5321_CR82","doi-asserted-by":"crossref","first-page":"4978","DOI":"10.1016\/j.eswa.2010.09.153","volume":"38","author":"H Ogura","year":"2011","unstructured":"Ogura H, Amano H, Kondo M (2011) Comparison of metrics for feature selection in imbalanced text classification. Expert Syst Appl 38(5):4978\u20134989","journal-title":"Expert Syst Appl"},{"issue":"3","key":"5321_CR83","doi-asserted-by":"crossref","first-page":"6826","DOI":"10.1016\/j.eswa.2008.08.006","volume":"36","author":"H Ogura","year":"2009","unstructured":"Ogura H, Amano H, Kondo M (2009) Feature selection with a measure of deviations from poisson in text categorization. Expert Syst Appl 36(3):6826\u20136832","journal-title":"Expert Syst Appl"},{"key":"5321_CR84","first-page":"05","volume":"1","author":"CL Devasena","year":"2011","unstructured":"Devasena CL, Sumathi T, Gomathi VV, Hemalatha M (2011) Effectiveness evaluation of rule based classifiers for the classification of iris data set. Bonfring Int J Man Mach Interface 1:05\u201309","journal-title":"Bonfring Int J Man Mach Interface"},{"issue":"5","key":"5321_CR85","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/0306-4573(88)90021-0","volume":"24","author":"G Salton","year":"1988","unstructured":"Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24(5):513\u2013523","journal-title":"Inf Process Manag"},{"key":"5321_CR86","unstructured":"Ranjana A, Madhura P (2013) A novel algorithm for automatic document clustering. In: Advance computing conference (IACC), 2013 IEEE 3rd International, pp 877\u2013882. IEEE"},{"key":"5321_CR87","doi-asserted-by":"crossref","unstructured":"Choi D Kim P (2012) Automatic image annotation using semantic text analysis. In: International conference on availability, reliability, and security, pp 479\u2013487. Springer","DOI":"10.1007\/978-3-642-32498-7_36"},{"issue":"5","key":"5321_CR88","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1002\/asi.21291","volume":"61","author":"C Huang","year":"2010","unstructured":"Huang C, Tianjun F, Chen H (2010) Text-based video content classification for online video-sharing sites. J Am Soc Inform Sci Technol 61(5):891\u2013906","journal-title":"J Am Soc Inform Sci Technol"},{"issue":"6","key":"5321_CR89","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/TKDE.2016.2522427","volume":"28","author":"B Tang","year":"2016","unstructured":"Tang B, He H, Baggenstoss PM, Kay S (2016) A bayesian classification approach using class-specific features for text categorization. IEEE Trans Knowl Data Eng 28(6):1602\u20131606","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5321_CR90","doi-asserted-by":"crossref","unstructured":"Rusland NF, Wahid N, Kasim S, Hafit H (2017) Analysis of na\u00efve bayes algorithm for email spam filtering across multiple datasets. In: IOP conference series: materials science and engineering, volume 226, p. 012091. IOP Publishing","DOI":"10.1088\/1757-899X\/226\/1\/012091"},{"key":"5321_CR91","unstructured":"Watkins CJCH (1989) Learning from delayed rewards. Ph. D. thesis, King\u2019s College, Cambridge"},{"key":"5321_CR92","doi-asserted-by":"crossref","unstructured":"Chitrakar R, Chuanhe H (2012) Anomaly detection using support vector machine classification with k-medoids clustering. In: 2012 Third Asian himalayas international conference on internet, pp 1\u20135. IEEE","DOI":"10.1109\/AHICI.2012.6408446"},{"key":"5321_CR93","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.eswa.2016.03.028","volume":"57","author":"A Tripathy","year":"2016","unstructured":"Tripathy A, Agrawal A, Rath SK (2016) Classification of sentiment reviews using n-gram machine learning approach. Expert Syst Appl 57:117\u2013126","journal-title":"Expert Syst Appl"},{"key":"5321_CR94","unstructured":"Bouvrie J (2006) Notes on convolutional neural networks. http:\/\/cogprints.org\/5869\/"},{"key":"5321_CR95","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press, Cambridge"},{"key":"5321_CR96","unstructured":"Lee CY, Gallagher PW, Tu Z (2016) Generalizing pooling functions in convolutional neural networks: mixed, gated, and tree. In: Artificial intelligence and statistics, pp 464\u2013472"},{"key":"5321_CR97","doi-asserted-by":"crossref","unstructured":"Ranzato M, Huang FJ, Boureau YL, LeCun Y (2007) Unsupervised learning of invariant feature hierarchies with applications to object recognition. In: 2007 IEEE conference on computer vision and pattern recognition, pp 1\u20138. IEEE","DOI":"10.1109\/CVPR.2007.383157"},{"key":"5321_CR98","doi-asserted-by":"crossref","unstructured":"Scherer D, M\u00fcller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: International conference on artificial neural networks, pp 92\u2013101. Springer","DOI":"10.1007\/978-3-642-15825-4_10"},{"key":"5321_CR99","unstructured":"Wang T, Wu DJ, Coates A , Ng AY (2012) End-to-end text recognition with convolutional neural networks. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012), pp 3304\u20133308. IEEE"},{"key":"5321_CR100","doi-asserted-by":"crossref","unstructured":"LeCun YA, Bottou L, Orr GB, M\u00fcller KR (2012) Efficient backprop. In: Neural networks: tricks of the trade, pp 9\u201348. Springer","DOI":"10.1007\/978-3-642-35289-8_3"},{"key":"5321_CR101","unstructured":"Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853"},{"key":"5321_CR102","unstructured":"Ramachandran P, Zoph B, Le QV (2017) Swish: a self-gated activation function. arXiv preprint arXiv:1710.05941, 7"},{"key":"5321_CR103","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"G Jiuxiang","year":"2018","unstructured":"Jiuxiang G, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recognit 77:354\u2013377","journal-title":"Pattern Recognit"},{"issue":"02","key":"5321_CR104","doi-asserted-by":"crossref","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-Based Syst 6(02):107\u2013116","journal-title":"Int J Uncertain Fuzziness Knowl-Based Syst"},{"key":"5321_CR105","unstructured":"Nwankpa C, Ijomah W, Gachagan A, Marshall S (2018) Activation functions: comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378"},{"key":"5321_CR106","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167"},{"key":"5321_CR107","unstructured":"Hinton GE, Nitish S, Alex K, Ilya S, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580"},{"issue":"1","key":"5321_CR108","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","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","journal-title":"J Mach Learn Res"},{"key":"5321_CR109","unstructured":"Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:1312.4400"},{"issue":"9","key":"5321_CR110","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","volume":"29","author":"W Rawat","year":"2017","unstructured":"Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29(9):2352\u20132449","journal-title":"Neural Comput"},{"key":"5321_CR111","unstructured":"Sutskever I, Martens J, Hinton GE (2011) Generating text with recurrent neural networks. In: ICML"},{"key":"5321_CR112","doi-asserted-by":"crossref","DOI":"10.1002\/047084535X","volume-title":"Recurrent neural networks for prediction: learning algorithms, architectures and stability","author":"D Mandic","year":"2001","unstructured":"Mandic D, Chambers J (2001) Recurrent neural networks for prediction: learning algorithms, architectures and stability. Wiley, Hoboken"},{"issue":"2","key":"5321_CR113","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Networks 5(2):157\u2013166","journal-title":"IEEE Trans Neural Networks"},{"key":"5321_CR114","unstructured":"Pascanu R, Mikolov T, Bengio Y (2013) On the difficulty of training recurrent neural networks. In: International conference on machine learning, pp 1310\u20131318"},{"key":"5321_CR115","unstructured":"Junyoung C, Caglar G, KyungHyun C, Yoshua B (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555"},{"key":"5321_CR116","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"5321_CR117","doi-asserted-by":"crossref","unstructured":"Conn AR, Scheinberg K , Vicente LN (2009) Introduction to derivative-free optimization. SIAM","DOI":"10.1137\/1.9780898718768"},{"issue":"4","key":"5321_CR118","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1023\/A:1008306431147","volume":"13","author":"DR Jones","year":"1998","unstructured":"Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Global Optim 13(4):455\u2013492","journal-title":"J Global Optim"},{"issue":"4","key":"5321_CR119","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s10898-008-9354-2","volume":"44","author":"J Villemonteix","year":"2009","unstructured":"Villemonteix J, Vazquez E, Walter E (2009) An informational approach to the global optimization of expensive-to-evaluate functions. J Global Optim 44(4):509","journal-title":"J Global Optim"},{"key":"5321_CR120","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-662-04378-3","volume-title":"The theory of evolution strategies","author":"H-G Beyer","year":"2001","unstructured":"Beyer H-G (2001) The theory of evolution strategies. Springer, Berlin"},{"issue":"1","key":"5321_CR121","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(1):281\u2013305","journal-title":"J Mach Learn Res"},{"key":"5321_CR122","unstructured":"Yin W, Kann K, Yu M, Sch\u00fctze H (2017) Comparative study of cnn and rnn for natural language processing. arXiv preprint arXiv:1702.01923"},{"key":"5321_CR123","unstructured":"Yoon K (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882"},{"key":"5321_CR124","unstructured":"Nal K, Edward G, Phil B (2014) A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188"},{"key":"5321_CR125","doi-asserted-by":"crossref","unstructured":"Yin W, Sch\u00fctze H (2016) Multichannel variable-size convolution for sentence classification. arXiv preprint arXiv:1603.04513","DOI":"10.18653\/v1\/K15-1021"},{"key":"5321_CR126","doi-asserted-by":"crossref","unstructured":"Zhang Y, Roller S, Wallace B (2016) Mgnc-cnn: a simple approach to exploiting multiple word embeddings for sentence classification. arXiv preprint arXiv:1603.00968","DOI":"10.18653\/v1\/N16-1178"},{"key":"5321_CR127","unstructured":"Yogatama D, Dyer C, Ling W, Blunsom P (2017) Generative and discriminative text classification with recurrent neural networks. arXiv preprint arXiv:1703.01898"},{"issue":"4","key":"5321_CR128","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/TASLP.2016.2520371","volume":"24","author":"H Palangi","year":"2016","unstructured":"Palangi H, Deng L, Shen Y, Gao J, He X, Chen J, Song X, Ward R (2016) Deep sentence embedding using long short-term memory networks: analysis and application to information retrieval. IEEE\/ACM Trans Audio Speech Language Process 24(4):694\u2013707","journal-title":"IEEE\/ACM Trans Audio Speech Language Process"},{"key":"5321_CR129","doi-asserted-by":"crossref","unstructured":"Vu NT, Adel H, Gupta P, Sch\u00fctze H (2016) Combining recurrent and convolutional neural networks for relation classification. arXiv preprint arXiv:1605.07333","DOI":"10.18653\/v1\/N16-1065"},{"key":"5321_CR130","unstructured":"Wen Y, Zhang W, Luo R, Wang J (2016) Learning text representation using recurrent convolutional neural network with highway layers. arXiv preprint arXiv:1606.06905,"},{"key":"5321_CR131","doi-asserted-by":"crossref","unstructured":"Wang J, Yu LC, Lai KR, Zhang X (2016) Dimensional sentiment analysis using a regional cnn-lstm model. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 2: Short Papers), pp 225\u2013230","DOI":"10.18653\/v1\/P16-2037"},{"key":"5321_CR132","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.eswa.2016.10.065","volume":"72","author":"T Chen","year":"2017","unstructured":"Chen T, Ruifeng X, He Y, Wang X (2017) Improving sentiment analysis via sentence type classification using bilstm-crf and cnn. Expert Syst Appl 72:221\u2013230","journal-title":"Expert Syst Appl"},{"key":"5321_CR133","unstructured":"Dauphin YN, Fan A, Auli M, Grangier D (2017) Language modeling with gated convolutional networks. In: Proceedings of the 34th international conference on machine learning-volume 70, pp 933\u2013941. JMLR. org"},{"key":"5321_CR134","doi-asserted-by":"crossref","unstructured":"Adel H, Sch\u00fctze H (2016) Exploring different dimensions of attention for uncertainty detection. arXiv preprint arXiv:1612.06549","DOI":"10.18653\/v1\/E17-1003"},{"key":"5321_CR135","unstructured":"Hoffmann J, Navarro O, Kastner F, Jan\u00dfen B, Hubner M (2017) A survey on cnn and rnn implementations. In: PESARO 2017: the seventh international conference on performance, safety and robustness in complex systems and applications"},{"key":"5321_CR136","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T (2015) Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1422\u20131432","DOI":"10.18653\/v1\/D15-1167"},{"key":"5321_CR137","doi-asserted-by":"crossref","unstructured":"Hermanto A, Adji TB, Setiawan NA(2015) Recurrent neural network language model for english-indonesian machine translation: Experimental study. In: 2015 International conference on science in information technology (ICSITech), pp 132\u2013136. IEEE, 2015","DOI":"10.1109\/ICSITech.2015.7407791"},{"key":"5321_CR138","doi-asserted-by":"crossref","unstructured":"Messina R, Louradour J (2015) Segmentation-free handwritten chinese text recognition with lstm-rnn. In: 2015 13th International conference on document analysis and recognition (icdar), pp 171\u2013175. IEEE, 2015","DOI":"10.1109\/ICDAR.2015.7333746"},{"issue":"3","key":"5321_CR139","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1109\/TASLP.2015.2400218","volume":"23","author":"M Sundermeyer","year":"2015","unstructured":"Sundermeyer M, Ney H, Schl\u00fcter R (2015) From feedforward to recurrent lstm neural networks for language modeling. IEEE\/ACM Trans Audio Speech Language Process 23(3):517\u2013529","journal-title":"IEEE\/ACM Trans Audio Speech Language Process"},{"key":"5321_CR140","doi-asserted-by":"crossref","unstructured":"Takase S, Suzuki J, Nagata M (2019) Character n-gram embeddings to improve rnn language models. arXiv preprint arXiv:1906.05506","DOI":"10.1609\/aaai.v33i01.33015074"},{"key":"5321_CR141","doi-asserted-by":"crossref","unstructured":"Viswanathan S, Kumar MA, Soman KP (2019) A sequence-based machine comprehension modeling using lstm and gru. In: Emerging research in electronics, computer science and technology, pp 47\u201355. Springer","DOI":"10.1007\/978-981-13-5802-9_5"},{"key":"5321_CR142","first-page":"2267","volume":"333","author":"S Lai","year":"2015","unstructured":"Lai S, Liheng X, Liu K, Zhao J (2015) Recurrent convolutional neural networks for text classification. AAAI 333:2267\u20132273","journal-title":"AAAI"},{"key":"5321_CR143","doi-asserted-by":"crossref","unstructured":"Chen G, Ye D, Xing Z, Chen J, Cambria E (2017) Ensemble application of convolutional and recurrent neural networks for multi-label text categorization. In: 2017 International joint conference on neural networks (IJCNN), pp 2377\u20132383. IEEE","DOI":"10.1109\/IJCNN.2017.7966144"},{"key":"5321_CR144","unstructured":"Zhou C, Sun C, Liu Z, Lau F (2015) A c-lstm neural network for text classification. arXiv preprint arXiv:1511.08630"},{"key":"5321_CR145","unstructured":"Wang X, Jiang W, Luo Z (2016) Combination of convolutional and recurrent neural network for sentiment analysis of short texts. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, pp 2428\u20132437"},{"key":"5321_CR146","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":"5321_CR147","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2016","unstructured":"Bojanowski P, Grave E, Joulin A, Mikolov T (2016) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135\u2013146","journal-title":"Trans Assoc Comput Linguist"},{"key":"5321_CR148","unstructured":"Jeffrey P, Richard S, Christopher M (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532\u20131543, Doha, Qatar, October (2014). Association for Computational Linguistics"},{"key":"5321_CR149","doi-asserted-by":"crossref","unstructured":"Ayinde BO, Inanc T, Zurada JM (2019) On correlation of features extracted by deep neural networks. arXiv preprint arXiv:1901.10900","DOI":"10.1109\/IJCNN.2019.8852296"},{"key":"5321_CR150","doi-asserted-by":"crossref","unstructured":"Bigi B (2003) Using kullback-leibler distance for text categorization. In: European conference on information retrieval, pp 305\u2013319. Springer","DOI":"10.1007\/3-540-36618-0_22"},{"key":"5321_CR151","doi-asserted-by":"crossref","unstructured":"Stehl\u00edk M, Ruiz MP, Stehl\u00edkov\u00e1 S, Lu Y (2020) On equidistant designs, symmetries and their violations in multivariate models. In: Contemporary experimental design, multivariate analysis and data mining, pp 217\u2013225. Springer","DOI":"10.1007\/978-3-030-46161-4_14"},{"key":"5321_CR152","unstructured":"Tang J, Alelyani S, Liu H (2014) Feature selection for classification: a review. Data Classification: Algorithms and Applications, pp 37"}],"updated-by":[{"DOI":"10.1007\/s00521-020-05435-z","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T00:00:00Z","timestamp":1603756800000}}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05321-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05321-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05321-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T07:20:14Z","timestamp":1632468014000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05321-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,24]]},"references-count":152,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["5321"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05321-8","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s00521-020-05435-z","asserted-by":"object"}]},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,24]]},"assertion":[{"value":"24 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2020","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00521-020-05435-z","URL":"https:\/\/doi.org\/10.1007\/s00521-020-05435-z","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}},{"value":"Corresponding author on the behalf of all authors declares that no conflict of interest is present.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"DSL Urdu news dataset, and other pre-processing resources will be available at Github repository ().","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and material"}},{"value":"The source code will be available at Github repository ().","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}