DT-GCNN: Dynamic Triplet Network with GRU-CNN for Enhanced Text Classification

DT-GCNN: Dynamic Triplet Network with GRU-CNN for Enhanced Text Classification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article DT-GCNN: Dynamic Triplet Network with GRU-CNN for Enhanced Text Classification Jiahui Li, Yuan Yang, Jian Sun, Fen Wang, Shuailiang Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5359853/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jul, 2025 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted 4 You are reading this latest preprint version Abstract Text classification is a crucial task in natural language processing, and deep learning models have demonstrated exceptional performance in this domain. However, many deep neural network models struggle to handle complex and imbalanced datasets, and their adaptability across multiple scenarios remains limited. To tackle these challenges, this paper proposes DT-GCNN, a model that integrates GRU for capturing sequence information and CNN for extracting local features. Furthermore, the model incorporates an adaptive soft-margin triplet loss function that dynamically adjusts triplet margins, thereby enhancing the learning quality of the embedding space. During the training process, intelligent algorithms are periodically employed to dynamically reconstruct triplets, thereby enhancing the model's generalization capability. This study conducts extensive experiments on eight datasets from various categories. The results demonstrate that DT-GCNN outperforms most existing baseline models, showing notable superiority in handling complex and imbalanced category tasks. The proposed method also significantly enhances generalization ability and stability, exhibiting excellent performance across diverse datasets. Text Classification Deep Learning Triplet Network Data Augmentation Embedding Space Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Jul, 2025 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted Editorial decision: Revision requested 11 Nov, 2024 Editor assigned by journal 05 Nov, 2024 Submission checks completed at journal 01 Nov, 2024 First submitted to journal 30 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5359853","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376921358,"identity":"b7afaa09-77d2-42a9-943b-6adad5a6039c","order_by":0,"name":"Jiahui Li","email":"","orcid":"","institution":"Ningxia Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Li","suffix":""},{"id":376921359,"identity":"77f91113-8361-4b0d-8444-d38d46b020d2","order_by":1,"name":"Yuan Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBAC+eYDMCYzjJWAX4vBAbgWNphSQloY4Fp4DIjVcsbwc8Gvw4n9s3u+PebdcZiBnz3HgOHnDtxa5BvOGEvP7DucOOPO2e2GM88cZpDseWPA2HsGjzUHzhhI8/YcTmy4kbtN4mPbYQaDGzkGzIxteLUY/wZpmX8j55lEIlCLPUEtJ86YSfP8OJy44UYOG8QWCQJaDM62lVnzNqQbb7yRZiY5sy2dR+LMs4KDvXi0yLczb77N88dadt6N5GfSvG3WcvztyRsf/MTnMAYOAwbGtmY4lwfsQXwaGBjYHzAw/KnDr2YUjIJRMApGNgAAo/VZw9IEzOoAAAAASUVORK5CYII=","orcid":"","institution":"Ningxia Normal University","correspondingAuthor":true,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Yang","suffix":""},{"id":376921360,"identity":"7073446a-9677-4287-a118-aa488e45c899","order_by":2,"name":"Jian Sun","email":"","orcid":"","institution":"Ningxia Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Sun","suffix":""},{"id":376921361,"identity":"319e94f2-1eb6-46ab-9411-a7a9c2816392","order_by":3,"name":"Fen Wang","email":"","orcid":"","institution":"Ningxia Normal University","correspondingAuthor":false,"prefix":"","firstName":"Fen","middleName":"","lastName":"Wang","suffix":""},{"id":376921362,"identity":"e7bb843d-6b80-42f9-ab4c-65c34941bed9","order_by":4,"name":"Shuailiang Chen","email":"","orcid":"","institution":"Ningxia Normal University","correspondingAuthor":false,"prefix":"","firstName":"Shuailiang","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-10-30 08:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5359853/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5359853/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13042-025-02769-9","type":"published","date":"2025-07-28T16:05:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88268234,"identity":"dcdda16b-7836-4b1d-93d3-373d952c3ff2","added_by":"auto","created_at":"2025-08-04 16:50:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":24422293,"visible":true,"origin":"","legend":"","description":"","filename":"DTGCNN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5359853/v1_covered_dce3ac59-3778-4d26-968f-8113e792ca79.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DT-GCNN: Dynamic Triplet Network with GRU-CNN for Enhanced Text Classification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Text Classification, Deep Learning, Triplet Network, Data Augmentation, Embedding Space","lastPublishedDoi":"10.21203/rs.3.rs-5359853/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5359853/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Text classification is a crucial task in natural language processing, and deep learning models have demonstrated exceptional performance in this domain. 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