{"paper_id":"336160d3-e5e1-4a43-864e-6cb87048a8eb","body_text":"Technical Skills Information Extraction From Resumes Using Advanced Natural Language Processing Model With Transfer Learning | 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 Article Technical Skills Information Extraction From Resumes Using Advanced Natural Language Processing Model With Transfer Learning Shyamala Devi, G Pavai Anand This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7744440/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 Matching job seekers with suitable job vacancies in the rapidly evolving job market is challenging. Enhancing the accuracy of job matching requires identifying specific skills and techniques essential for each vacancy. This study proposes an innovative algorithm that utilizes Transfer Learning combined with advanced Natural Language Processing (NLP) models specifically SpaCy, BERT (Bidirectional Encoder Representations from Transformers), and XLNet to automatically detect and extract skills and techniques from job vacancy descriptions. The algorithm initiates by pre-processing job descriptions through tokenization and text cleaning. Next, SpaCy is used for part-of-speech tagging, entity recognition, and syntactic parsing to extract relevant keywords and phrases. These pre-processing steps effectively reduce noise and improve the quality of extracted data. BERT and XLNet, two cutting-edge transformer-based models, are then fine-tuned on a large corpus of job description data. This fine-tuning adapts the models to the specific language and context of job descriptions, enabling them to encode reprocessed descriptions into contextual embeddings. At the algorithm core is the identification of skills and techniques within job descriptions by leveraging these contextual embeddings. A combination of named entity recognition, keyword extraction, and context-based keyword filtering is employed to identify essential skills and techniques for each role. The algorithm’s performance is evaluated using precision, recall, and F1-score metrics on labeled job description datasets. Results show the algorithm effectiveness in detecting skills and techniques, providing significant benefits in automating and enhancing the job matching process. By accurately identifying required skills, job seekers can connect with more relevant opportunities, and employers can streamline recruitment. This approach has the potential to improve job market efficiency, leading to more successful placements. Physical sciences/Engineering Physical sciences/Mathematics and computing Skill Detection Technique Detection Job Vacancies Transfer Learning SpaCy BERT (Bidirectional Encoder Representations from Transformers) XLNet NLP (Natural Language Processing) Named Entity Recognition 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. 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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-7744440\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":535308097,\"identity\":\"1079df3a-15d2-4fdf-880b-bf13225a6119\",\"order_by\":0,\"name\":\"Shyamala 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Using Advanced Natural Language Processing Model With Transfer Learning\\u003c/p\\u003e\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"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\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research 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Enhancing the accuracy of job matching requires identifying specific skills and techniques essential for each vacancy. This study proposes an innovative algorithm that utilizes Transfer Learning combined with advanced Natural Language Processing (NLP) models specifically SpaCy, BERT (Bidirectional Encoder Representations from Transformers), and XLNet to automatically detect and extract skills and techniques from job vacancy descriptions. The algorithm initiates by pre-processing job descriptions through tokenization and text cleaning. Next, SpaCy is used for part-of-speech tagging, entity recognition, and syntactic parsing to extract relevant keywords and phrases. These pre-processing steps effectively reduce noise and improve the quality of extracted data. BERT and XLNet, two cutting-edge transformer-based models, are then fine-tuned on a large corpus of job description data. This fine-tuning adapts the models to the specific language and context of job descriptions, enabling them to encode reprocessed descriptions into contextual embeddings. At the algorithm core is the identification of skills and techniques within job descriptions by leveraging these contextual embeddings. A combination of named entity recognition, keyword extraction, and context-based keyword filtering is employed to identify essential skills and techniques for each role. The algorithm\\u0026rsquo;s performance is evaluated using precision, recall, and F1-score metrics on labeled job description datasets. Results show the algorithm effectiveness in detecting skills and techniques, providing significant benefits in automating and enhancing the job matching process. By accurately identifying required skills, job seekers can connect with more relevant opportunities, and employers can streamline recruitment. 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