{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:31:11Z","timestamp":1785501071845,"version":"3.56.0"},"reference-count":35,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3031200","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T19:28:33Z","timestamp":1602703713000},"page":"188036-188043","source":"Crossref","is-referenced-by-count":19,"title":["Natural Language Word Prediction Model Based on Multi-Window Convolution and Residual Network"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7506-9895","authenticated-orcid":false,"given":"Jingyun","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexiang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.21236\/ADA273556"},{"key":"ref32","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref31","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"xavier","year":"2010","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref30","first-page":"109","article-title":"Sentiment analysis based on multi-channel convolution and bi-directional GRU with attention mechanism","volume":"33","author":"yuan","year":"2019","journal-title":"J Chin Inf Process"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/SLT.2012.6424228"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1285"},{"key":"ref10","first-page":"1642","article-title":"Non-stationary multivariate time series prediction with MIX gated unit","volume":"56","author":"liu","year":"2019","journal-title":"J Comput Res Develop"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00572"},{"key":"ref12","first-page":"4189","article-title":"Recurrent highway networks","author":"zilly","year":"2016","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref13","first-page":"1","article-title":"Residual connections encourage iterative inference","author":"jastrzebski","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref14","article-title":"Recurrent neural network regularization","author":"wojciech","year":"2014","journal-title":"arXiv 1409 2329"},{"key":"ref15","first-page":"1","article-title":"On the state of the art of evaluation in neural language models","author":"melis","year":"2018","journal-title":"Proc ICLR Conf Blind Submission"},{"key":"ref16","first-page":"1","article-title":"Regularizing and optimizing LSTM language models","author":"stephen","year":"2017","journal-title":"Proc Int Conf Learn Represent (ICLR)ICLR"},{"key":"ref17","first-page":"1","article-title":"Recurrent batch normalization","author":"cooijmans","year":"2016","journal-title":"Proc Int Conf Learn Represent (ICLR)ICLR"},{"key":"ref18","first-page":"7694","article-title":"Understanding batch normalization","author":"bjorck","year":"2018","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref19","first-page":"1","article-title":"A mean field theory of batch normalization","author":"yang","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref28","first-page":"2342","article-title":"An empirical exploration of recurrent network architectures","author":"rafal","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582924"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"ref6","first-page":"95","article-title":"Relation extraction method based on relation trigger words and single-layer GRU model","volume":"58","author":"wang","year":"2020","journal-title":"Journal of Jilin University Sci Ed"},{"key":"ref29","first-page":"160","article-title":"A unified architecture for natural language processing: Deep neural networks with multitask learning","author":"ronan","year":"2008","journal-title":"Proc 25th Int Conf Mach Learn"},{"key":"ref5","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"2014","journal-title":"arXiv 1412 3555"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s11633-016-1006-2"},{"key":"ref7","first-page":"3558","article-title":"Text sentiment classification model based on BiGRU-attention neural network","author":"wang","year":"2019","journal-title":"Appl Res Comput"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1080"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2019.2899603"},{"key":"ref1","first-page":"22","article-title":"A survey on statistical language models","volume":"30","author":"xing","year":"2003","journal-title":"Comput Sci"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2953957"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ROBIO.2018.8665330"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.149"},{"key":"ref23","first-page":"64","article-title":"Combination of convolutional recurrent neural network","volume":"33","author":"song","year":"2019","journal-title":"J Chin Inf Process"},{"key":"ref26","first-page":"276","article-title":"Efficient face recognition algorithm using global deep separable convolutional and residual network","volume":"41","author":"chen","year":"2019","journal-title":"Journal of Wuhan Institute of Science and Technology"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09223675.pdf?arnumber=9223675","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:57:43Z","timestamp":1642003063000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9223675\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3031200","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}