Bridging Behavioral Gaps: Automatic Extrapolation of Concreteness Norms for Arabic and English with a k-Nearest Neighbor Approach

Bridging Behavioral Gaps: Automatic Extrapolation of Concreteness Norms for Arabic and English with a k-Nearest Neighbor Approach | 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 Bridging Behavioral Gaps: Automatic Extrapolation of Concreteness Norms for Arabic and English with a k-Nearest Neighbor Approach Marina Aziz, Urban Knupleš, Diego Frassinelli, Sabine Schulte im Walde This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7858980/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This article addresses the automatic extrapolation of concreteness norms for nouns in English and Modern Standard Arabic, a low-resource language with different cultural characteristics. The main goal of this study is to develop computational methods for reliably estimating the degree of word concreteness (e.g. apple vs. justice), supporting applications in psycholinguistics and natural language processing. To this end, we introduce a novel dataset of 202 Arabic nouns, rated for concreteness by humans and aligned with established English norms. Using both English and Arabic resources as gold-standards, we compare the output of k-Nearest Neighbors (KNN) regression models built on FastText and transformer-based embeddings with ratings generated by ChatGPT. The KNN models highly correlate with human ratings for both English and Arabic, whereas ChatGPT, while consistent across runs, yield lower correlations. These results clearly show that KNN is the most accurate method for concreteness estimation in low resources settings. We conclude with quantitative and qualitative analyses of systematic patterns in model behavior and cross-cultural differences in concreteness norms. concreteness norms Arabic English K-Nearest Neighbors word embeddings FastText transformer models ChatGPT lexical semantics psycholinguistics norm extrapolation Full Text Additional Declarations No competing interests reported. Supplementary Files code.zip Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Dec, 2025 Reviews received at journal 13 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviews received at journal 25 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers invited by journal 22 Oct, 2025 Editor assigned by journal 22 Oct, 2025 Submission checks completed at journal 17 Oct, 2025 First submitted to journal 14 Oct, 2025 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. 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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-7858980","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538413886,"identity":"e5c6b324-bfe8-4564-8352-93da420faa94","order_by":0,"name":"Marina Aziz","email":"","orcid":"","institution":"University of Stuttgart","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"Aziz","suffix":""},{"id":538413887,"identity":"857bd22d-5efe-4578-afe4-3204cb6e6a4d","order_by":1,"name":"Urban Knupleš","email":"","orcid":"","institution":"University of 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