Ekantipur-15Y: A Longitudinal Benchmark Corpus and Semantic Analysis of Nepali News (2010 - 2025)

Ekantipur-15Y: A Longitudinal Benchmark Corpus and Semantic Analysis of Nepali News (2010 - 2025) | 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 Ekantipur-15Y: A Longitudinal Benchmark Corpus and Semantic Analysis of Nepali News (2010 - 2025) Diwash Mainali, Utsav Mainali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8630749/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 This paper introduces Ekantipur-15Y, a long-scale longitudinal corpus of Nepali news articles spanning from 2010 to 2025. As Nepali is considered a low-resource language, the lack of a clean and temporally diverse dataset has been a barrier for the development of robust Natural Language Processing (NLP) models. We collected and cleaned 109,704 unique articles with approximately 14.3 million tokens from Ekantipur. The corpus is validated using Zipf's law confirming linguistic integrity and Heap's law demonstrating continuous growth of vocabulary without plateauing. Furthermore, the semantic analysis successfully detects the major historical events in the context of Nepal, including the 2015 Earthquake and the COVID-19 pandemic validating the accuracy of the dataset. Finally, a baseline is established for text classification, where a Linear Support Vector Machine (SVM) achieves an accuracy of 74.50%, significantly outperforming Naive Bayes and Logistic Regression. Nepali NLP Low-Resource Languages Longitudinal Corpus Text Classification Event Detection Full Text Additional Declarations No competing interests reported. Supplementary Files Baselinemodel.png highlevelcrawlerarchitecture.png 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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