Bi-Directional Context-Aware Networks for the Nested Named Entity Recognition

Bi-Directional Context-Aware Networks for the Nested Named Entity Recognition | 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 Bi-Directional Context-Aware Networks for the Nested Named Entity Recognition Yao Li, Ningsheng Liao, He Yan, Ye Zhang, Xu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3828951/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract A prevalent method for nested named entity recognition involves enumerating all potential entity spans within a given sentence, considering these spans as potential entity types, and subsequently subjecting them to classification. Nevertheless, this span-centric approach traditionally undertakes the decoding of each entity span independently, neglecting the underlying semantic connections that exist between these spans, as well as the valuable information pertaining to the specific locations of the head and tail tokens within each span. Therefore, we introduced a bidirectional context-aware network specifically engineered to model and elucidate the semantic relationships that underpin these spans. Additionally, we improve the bi-affine mechanism by introducing rotational position coding to capture the relative position information between the head and tail markers. We carried out experiments on three nested datasets, producing convincing outcomes that firmly demonstrate the superior performance of our model compared to existing models, particularly in terms of F1 scores. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Apr, 2024 Reviews received at journal 02 Apr, 2024 Reviews received at journal 21 Mar, 2024 Reviewers agreed at journal 10 Mar, 2024 Reviewers agreed at journal 10 Mar, 2024 Reviewers invited by journal 10 Mar, 2024 Editor assigned by journal 21 Feb, 2024 Editor invited by journal 03 Jan, 2024 Submission checks completed at journal 03 Jan, 2024 First submitted to journal 02 Jan, 2024 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-3828951","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":265277518,"identity":"a050b30d-d85a-4b93-8bfd-0b393a66416f","order_by":0,"name":"Yao Li","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Li","suffix":""},{"id":265277520,"identity":"b11292b4-3e8c-480c-8762-af522fe88e1c","order_by":1,"name":"Ningsheng Liao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYBACPmYQaQDEEiBGBVSYB48WNlQtZ0BChLTAWSAtjG3EaGFnfvi4oOCO3fzZzccefp1nJ7vhfgPjg7dtDPLmOB3GZmw8w+BZ8oY7x9KNZbclG284xsBsOLeNwXBnA06/mEnzGBxONpDIMZOW3HYgEaiFTZq3jSHB4AAuLezfwFrkZ+R/k5acA9bC/hu/Fh6wLXYMN3LYJD82QGxhJqCl2BioJcHgRpqZNMOxZOOZxxKbJeeckzDcgEMLP//xjY95/hy2l5+R/EzyR42dbN/hwwc/vCmzkcdlCwwkNgAJZmB0MDaAEDRm8QJ7EMH4gwGifhSMglEwCkYBMgAAt9VVE3HnW1UAAAAASUVORK5CYII=","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Ningsheng","middleName":"","lastName":"Liao","suffix":""},{"id":265277522,"identity":"e2fc4e73-f5d0-4167-a40d-d3c752335092","order_by":2,"name":"He Yan","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Yan","suffix":""},{"id":265277524,"identity":"3a582956-27a3-4953-9c76-f00e75be643c","order_by":3,"name":"Ye Zhang","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Zhang","suffix":""},{"id":265277526,"identity":"b4ea760c-2755-4cc4-a825-bde9f065682d","order_by":4,"name":"Xu Wang","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-01-02 08:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3828951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3828951/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49232294,"identity":"4b5b5e53-e195-4ac9-b89b-d3adea6ba3fc","added_by":"auto","created_at":"2024-01-05 16:41:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1419530,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3828951/v1_covered_69dab9d1-c007-4a74-9797-95e7eb4a35d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bi-Directional Context-Aware Networks for the Nested Named Entity Recognition","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3828951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3828951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"A prevalent method for nested named entity recognition involves enumerating all potential entity spans within a given sentence, considering these spans as potential entity types, and subsequently subjecting them to classification. 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