BERT-Based Fine-Tuning for Efficient Context Similarity Analysis

BERT-Based Fine-Tuning for Efficient Context Similarity Analysis | 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 BERT-Based Fine-Tuning for Efficient Context Similarity Analysis Saksha Gotad, Mahesh Bhandari, Aaryan Gharat, Sunita Naik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6345204/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Contextual similarity detection has emerged as a critical need in academic publishing, particularly for high-impact journals such as IEEE. Traditional plagiarism detection methods are often insufficient, as they primarily rely on exact text matching, making it easy to bypass them through rephrasing. This study presents a fine-tuned BERT model designed specifically to evaluate the contextual similarity of academic papers. Utilizing a curated dataset of 8,000 research papers, equally sourced from arXiv and Semantic Scholar, the model achieved an accuracy of 92.52%. The proposed approach aims to enhance the integrity of academic publishing by effectively identifying duplicate content, even when paraphrased. This paper outlines the methodology, experimental results, and implications for improving originality checks in academic publishing along with reducing the chances of duplicate papers being published in any journals. BERT Context Similarity Detection Machine Learning Plagiarism Detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6345204","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436361289,"identity":"0ea684b5-22fa-4df8-83f8-6c0cc9dc903e","order_by":0,"name":"Saksha Gotad","email":"","orcid":"","institution":"VIVA Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Saksha","middleName":"","lastName":"Gotad","suffix":""},{"id":436361290,"identity":"0d109d17-b3c4-4d90-baff-492ba9d8b192","order_by":1,"name":"Mahesh Bhandari","email":"data:image/png;base64,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","orcid":"","institution":"VIVA Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Mahesh","middleName":"","lastName":"Bhandari","suffix":""},{"id":436361291,"identity":"ef06fe75-4c8a-4098-b30b-a7bc521d1a28","order_by":2,"name":"Aaryan Gharat","email":"","orcid":"","institution":"VIVA Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Aaryan","middleName":"","lastName":"Gharat","suffix":""},{"id":436361292,"identity":"1ae9f7c5-46fd-4b3f-8b4a-6027a435a35f","order_by":3,"name":"Sunita Naik","email":"","orcid":"","institution":"VIVA Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sunita","middleName":"","lastName":"Naik","suffix":""}],"badges":[],"createdAt":"2025-03-31 13:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6345204/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6345204/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80198526,"identity":"d76bece1-9658-434b-8537-eb5482d00ad4","added_by":"auto","created_at":"2025-04-09 06:16:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":298836,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchPaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6345204/v1_covered_b7c940ae-bc55-40a7-99c9-ef51ef2d2ba4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"BERT-Based Fine-Tuning for Efficient Context Similarity Analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"BERT, Context Similarity Detection, Machine Learning, Plagiarism Detection","lastPublishedDoi":"10.21203/rs.3.rs-6345204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6345204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e Contextual similarity detection has emerged as a critical need in academic publishing, particularly for high-impact journals such as IEEE. 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