A Retrieval Model with Contextual Correlation Analysis for Verbose Queries

A Retrieval Model with Contextual Correlation Analysis for Verbose Queries | 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 A Retrieval Model with Contextual Correlation Analysis for Verbose Queries Dipannita Podder, Jiaul H. Paik, Pabitra Mitra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6970571/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2026 Read the published version in Journal of Intelligent Information Systems → Version 1 posted 11 You are reading this latest preprint version Abstract Retrieving relevant documents using verbose queries is a key challenge in information retrieval, as such queries often include extraneous terms. Traditional retrieval models treat all query terms equally, which limits their effectiveness. Existing methods for verbose queries are typically supervised or rely on costly two-stage ranking pipelines.We propose a fully unsupervised, single-phase retrieval model that estimates the centrality of each query term by analyzing its contextual correlation with the entire query. A fully connected term graph is constructed, where edge weights capture the relative correlation of each term with the query context compared to others. Centrality scores are computed via power iteration over this graph. Dense representations of query terms and context are obtained using a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model.To further reduce the influence of non-informative document terms, an additional weight based on term information content is introduced. These two weights are combined and integrated into a modified Markov Random Field Sequential Dependence Model (SDM) for ranking.Experiments show that our model outperforms unsupervised baselines, performs comparably to supervised baselines, and surpasses several neural rankers in zero-shot settings. Comparable results with both GloVe and BERT embeddings highlight its embedding independence nature. The model shows larger gains on longer queries, modest improvements on shorter ones, but never underperforms SDM.Therefore, the model’s independence from relevance judgments and top-ranked documents, along with its consistent, embedding-agnostic performance across query lengths, makes it well-suited for low-resource scenarios. Verbose Query Information Retrieval Query Term Weighting Document Ranking Contextual Embeddings Dense Representation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jan, 2026 Read the published version in Journal of Intelligent Information Systems → Version 1 posted Editorial decision: Revision requested 24 Aug, 2025 Reviews received at journal 07 Aug, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviews received at journal 11 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers invited by journal 08 Jul, 2025 Editor assigned by journal 02 Jul, 2025 Submission checks completed at journal 02 Jul, 2025 First submitted to journal 25 Jun, 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. 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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-6970571","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483248515,"identity":"c4491c87-dae5-4858-a6bc-6e36ed6c2195","order_by":0,"name":"Dipannita Podder","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDCCw2BSgoGBvbHhAMMBqGgCEVokGHgONhw4QJQWqCIJBgmgMrgWfIDvOO/Bz4U7LOr4Zz5uPPzhTG0eA/vhBwwPd+DWInmYL1l65hkJCYnbiUCH3ThezMCTZsCQeAa3FoPDPAbSvG1Av4C1fDiW2MCQw8CQ2IZXi/FvkBb5mwehWvjfENRiBrbF4AYjyGE1iQ0SBGwB+iXNGqhFcuMZoMPOnDlQzCbxzOAAPi18588evs3bVscvd/z44w8Vx+ry+PmTHz78iUcLAwMPCu9wAhsDPLKI01KXgF/1KBgFo2AUjEQAAIUNW6qdR0XZAAAAAElFTkSuQmCC","orcid":"","institution":"Indian Institute of Technology Kharagpur","correspondingAuthor":true,"prefix":"","firstName":"Dipannita","middleName":"","lastName":"Podder","suffix":""},{"id":483248516,"identity":"a188d30b-cdbd-4865-8eb6-a6676d96bc44","order_by":1,"name":"Jiaul H. 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