{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T16:06:04Z","timestamp":1779120364204,"version":"3.51.4"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T00:00:00Z","timestamp":1768608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Owing to the inherent limitations of convolutional neural networks (CNNs), text classification has historically fallen short of the performance achieved by 2D CNNs in computer vision. Furthermore, the intrinsic complexity and variability of textual data make shallow classification models insufficient for capturing deeper and more abstract semantic representations. To overcome these challenges, this study adopts a constrained 2D CNN coupled with a vision-inspired attention mechanism to extract informative textual features, thereby constructing a novel representation termed a multi-scale textual feature map. Building on the demonstrated effectiveness of ResNet and SENet in deep architectures, we further designed a competitive attention mechanism specifically tailored for multi-scale textual feature maps and introduced the Deep 2D CNN (D3CNN). Experimental results indicate that D3CNN yields substantial performance improvements over baseline models across five benchmark datasets, approaching the accuracy of state-of-the-art Transformer-based architectures while maintaining a favorable balance between performance and computational efficiency.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaf151","type":"journal-article","created":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T12:44:51Z","timestamp":1766666691000},"page":"888-904","source":"Crossref","is-referenced-by-count":0,"title":["The D3CNN text classification model based on multi-scale textual feature maps"],"prefix":"10.1093","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4180-059X","authenticated-orcid":false,"given":"Chen","family":"Kang","sequence":"first","affiliation":[{"name":"Faculty of Artificial Intelligence in Education , Central China Normal University, 152 Luoyu Road, Hongshan District, Wuhan, Hubei 430079,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0409-1918","authenticated-orcid":false,"given":"Kunyan","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Education , Shaanxi Normal University, 199 South Chang'an Road, Yanta District, Xi'an, Shaanxi 710062,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,1,17]]},"reference":[{"key":"2026051811064442200_ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439726","article-title":"Deep learning\u2013based text classification: a comprehensive review","volume":"54","author":"Minaee","year":"2021","journal-title":"ACM Comput Surv (CSUR)"},{"key":"2026051811064442200_ref2","doi-asserted-by":"publisher","first-page":"9401","DOI":"10.1007\/s10462-023-10393-8","article-title":"A review of semi-supervised learning for text classification","volume":"56","author":"Duarte","year":"2023","journal-title":"Artif Intell Rev"},{"key":"2026051811064442200_ref3","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1006\/jvci.1996.0008","article-title":"Internet categorization and search: a self-organizing approach","volume":"7","author":"Chen","year":"1996","journal-title":"J Vis Commun Image Represent"},{"key":"2026051811064442200_ref4","doi-asserted-by":"crossref","first-page":"3000","DOI":"10.1093\/comjnl\/bxac144","article-title":"Rscoewr: radical-based sentiment classification of online education website reviews","volume":"66","author":"Li","year":"2023","journal-title":"Comput J"},{"key":"2026051811064442200_ref5","doi-asserted-by":"crossref","first-page":"107614","DOI":"10.1016\/j.asoc.2021.107614","article-title":"Berconvonet: a deep learning framework for fake news classification","volume":"110","author":"Choudhary","year":"2021","journal-title":"Appl Soft Comput"},{"key":"2026051811064442200_ref6","doi-asserted-by":"publisher","first-page":"120977","DOI":"10.1016\/j.eswa.2023.120977","article-title":"Email spam detection using hierarchical attention hybrid deep learning method","volume":"233","author":"Zavrak","year":"2023","journal-title":"Expert Syst Appl"},{"key":"2026051811064442200_ref7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3560260","article-title":"QA dataset explosion: a taxonomy of nlp resources for question answering and reading comprehension","volume":"55","author":"Rogers","year":"2023","journal-title":"ACM Comput Surv"},{"key":"2026051811064442200_ref8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3571730","article-title":"Survey of hallucination in natural language generation","volume":"55","author":"Ji","year":"2023","journal-title":"ACM Comput Surv"},{"key":"2026051811064442200_ref9","article-title":"Distributed representations of words and phrases and their compositionality","volume":"26","author":"Mikolov","year":"2013","journal-title":"Adv Neural Inform Process Syst"},{"key":"2026051811064442200_ref10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"2026051811064442200_ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1167"},{"key":"2026051811064442200_ref12"},{"key":"2026051811064442200_ref13","author":"","journal-title":"Proc. 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