{"paper_id":"4231b7e9-2771-4d4f-b222-e8cd0c86da57","body_text":"Authorship Attribution in Hindi Literary Texts: An Exploration of Traditional Linguistic Approaches and Experimentation with Multilingual BERT | 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 Authorship Attribution in Hindi Literary Texts: An Exploration of Traditional Linguistic Approaches and Experimentation with Multilingual BERT Dhruve Kiyawat, Vibha Tiwari, Ocean Agarwal, Tijil Dubey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5462231/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 Authorship attribution, the attribution of a manuscript to an author, has been successfully carried out in English Literature. This study hypothesizes that authorship attribution is better encoded in the semantic structures of Hindi literature rather than its linguistic features. The work serves as a venture into the aforementioned notion by contrasting m-BERT with traditional stylometric methods such as n-grams, Bag of Words (BoW), and Term Frequency-Inverse Document Frequency(TF-IDF), on a curated dataset of Hindi stories. Our findings reveal that, in the domain of authorship attribution for Hindi stories, traditional methods exhibit greater effectiveness compared to the modern m-BERT approach. The dataset preparation, research methodology, and results have been elucidated, as well as a thorough discussion on the insights derived from the findings regarding the future scope of this work. Authorship Attribution mBERT TF-IDF Bag of Words NLP Hindi Literature 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. 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