Joint Entity and Relation Extraction Algorithm for Historical Literature Retrieval

Joint Entity and Relation Extraction Algorithm for Historical Literature Retrieval | 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 Joint Entity and Relation Extraction Algorithm for Historical Literature Retrieval Hang Zhao, Runjia Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7364552/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 Historical literature contains abundant structured knowledge. However, its strong unstructured nature, complex semantic associations, and high entity ambiguity limit the effectiveness of traditional pipeline methods due to error accumulation, and single-task models due to semantic fragmentation. To address this issue, this paper puts forward a joint entity and relation extraction algorithm tailored for historical literature. On this basis, the model integrates Gaussian-based local enhancement and a multi-channel knowledge matching module. Experimental results show that the proposed algorithm achieves a highest F1 score of 0.96, demonstrating excellent overall performance. The model reaches an F1 score of 89.7% on the named entity recognition task, significantly outperforming the baseline score of 78.2%. For the relation classification task, the F1 score reaches 83.2%, which is 11.2% and 8.7% higher than those of the pipeline method and mainstream joint extraction models, respectively. Moreover, the training time is as low as 450.5 s, and the inference time drops to 20.8 ms, achieving a good balance between accuracy and efficiency. Ablation experiments confirm the effectiveness of the cross-task attention and Gaussian enhancement modules. This work offers an efficient approach for structured knowledge extraction from historical literature and holds great potential for advancing digital humanities research and large-scale historical knowledge base construction. Physical sciences/Engineering Physical sciences/Mathematics and computing Historical literature Entity and relation extraction Multi-channel matching mechanism Joint model Gaussian enhancement Knowledge extraction 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7364552","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":531372223,"identity":"d0637e2f-6f90-456f-83fe-c1f99f1704ba","order_by":0,"name":"Hang 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