Event Type and Relationship Extraction Based on Dependent Syntactic Semantic Augmented Graph Networks

Event Type and Relationship Extraction Based on Dependent Syntactic Semantic Augmented Graph Networks | 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 Event Type and Relationship Extraction Based on Dependent Syntactic Semantic Augmented Graph Networks Min Zuo, Zexi Song, Yueheng Liu, Qingchuan Zhang, Yuanyuan Cai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3923678/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 In the domain of natural language processing, tasks such as trigger word extraction, event type recognition, and event relation extraction occupy pivotal positions. These tasks enable the discernment and extraction of salient information through profound analysis of textual content, thereby not only extricating key event data but also facilitating a deeper understanding of the semantic essence of the text. The process of extracting trigger words and event types often grapples with the complexities posed by polysemous words and intricate sentence structures, which can lead to deficiencies in the semantic representation of sentences. Addressing this challenge, this paper introduces a Dependency Syntactic Analysis model and proposes a novel framework Event Type Extraction Model base on Gravitational Network with Enhanced Dependency Semantics(GNEDS) that elucidates the intricate relationships and structures among words in a sentence. This approach significantly enriches the comprehension of contextual information, thereby enabling more precise identification of trigger words and their contextual affiliations, and consequently enhancing the text’s semantic representation.Furthermore, in the realm of event relation recognition, while traditional research has predominantly concentrated on intrasentential event relations, real world texts frequently exhibit event relations that span multiple sentences and entail complex contextual and implicit reasoning. This complexity often results in the sub-optimal performance of existing models in cross-sentence event relation extraction tasks. To overcome this limitation, this study introduces Graph Convolutional Neural Network (GCN) and the innovative concept of document nodes. Consequently, a Document Event Relationship Extraction based on Graph Convolutional Network with Enhanced Dependency Semantics(GCNEDS) is proposed that extends the scope to encompass a broader spectrum of global information, thereby enabling the amalgamation of textual information across various levels. This model is adept at capturing the long-distance dependencies between individual sentences within a document with greater accuracy, representing a significant advancement in the field of event type and relationship extraction based on dependent syntactic-semantic augmented graph networks. Event type extraction Event relation extraction Dependent syntax GCN Gravitational network BERT Full Text 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. 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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-3923678","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273560526,"identity":"2128f47f-e3e3-441d-9dce-3cca8d5a2e04","order_by":0,"name":"Min Zuo","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Zuo","suffix":""},{"id":273560527,"identity":"d5533e96-6433-41ce-b115-e80b067c4be4","order_by":1,"name":"Zexi Song","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Zexi","middleName":"","lastName":"Song","suffix":""},{"id":273560528,"identity":"a9cf18fc-ce4c-441d-8abc-c5456f1b97de","order_by":2,"name":"Yueheng Liu","email":"","orcid":"","institution":"Beijing Academy of TCM Beauty Supplements Co.","correspondingAuthor":false,"prefix":"","firstName":"Yueheng","middleName":"","lastName":"Liu","suffix":""},{"id":273560529,"identity":"78884559-5e0f-4bab-acb1-1f5b8873d477","order_by":3,"name":"Qingchuan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIie3QoQ7CMBCA4WuaFHNDVzEeYQTLw5SQMLP5epLDLGgEgbdAt1mCKk+AWbIEhZicg6EICeIkor+quC+5K0As9qe5zi4marRxbCH8PqznY7wYNpF1QvXyqIspbz7drpoag8wJCoDenhlbVffMH6wqCa5OVOHG2EoX4B4BSxI7IwUxiHqThHSuJGY8ggPxCWVGKS7RONyyD2ZGqIxn3TL8WNt19pmmp9Y3vWUQAPN5Os78N4nFYrHYz145/z2NasXZygAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":true,"prefix":"","firstName":"Qingchuan","middleName":"","lastName":"Zhang","suffix":""},{"id":273560530,"identity":"1268cf3f-578a-4ba6-b7c8-ed9e3984ab18","order_by":4,"name":"Yuanyuan Cai","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Cai","suffix":""},{"id":273560531,"identity":"ddf7f59f-0e74-4edd-b665-bb5a22302622","order_by":5,"name":"Di Wu","email":"","orcid":"","institution":"Beijing Academy of TCM Beauty Supplements Co.","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-02-03 11:05:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3923678/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3923678/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65093061,"identity":"931c3165-d033-4195-bb57-963ad8182d66","added_by":"auto","created_at":"2024-09-23 14:12:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":492660,"visible":true,"origin":"","legend":"","description":"","filename":"EventTypeandRelationshipExtraction1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3923678/v1_covered_b446721f-74bc-48a8-bf16-d4a7e3cc30e3.pdf"}],"financialInterests":"","formattedTitle":"Event Type and Relationship Extraction Based on Dependent Syntactic Semantic Augmented Graph Networks","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":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Event type extraction, Event relation extraction, Dependent syntax, GCN, Gravitational network, BERT","lastPublishedDoi":"10.21203/rs.3.rs-3923678/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3923678/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the domain of natural language processing, tasks such as trigger word extraction, event type recognition, and event relation extraction occupy pivotal positions. These tasks enable the discernment and extraction of salient information through profound analysis of textual content, thereby not only extricating key event data but also facilitating a deeper understanding of the semantic essence of the text. The process of extracting trigger words and event types often grapples with the complexities posed by polysemous words and intricate sentence structures, which can lead to deficiencies in the semantic representation of sentences. Addressing this challenge, this paper introduces a Dependency Syntactic Analysis model and proposes a novel framework Event Type Extraction Model base on Gravitational Network with Enhanced Dependency Semantics(GNEDS) that elucidates the intricate relationships and structures among words in a sentence. 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