Fine-grained extraction of geospatial and temporal information from Chinese historical newspapers.
Historical newspapers are invaluable repositories of comprehensive knowledge, capturing the essence of diverse societal shifts and pivotal events across varying epochs. By scrutinizing and identifying intricate details such as place names, locations, dates, and a diverse array of Points of Interest...
| Publicado en: | Digital Scholarship in the Humanities Vol. 40; no. 2; pp. 601 - 617 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186085067&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186085067 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Jun2025 vid: 40 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 186085067 10.1093/llc/fqaf025 ppf: 601 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.3MB tig: atl: Fine-grained extraction of geospatial and temporal information from Chinese historical newspapers. aug: au: Sun, Shaodan Qin, Xugong affil: School of Information Management, Nanjing Agricultural University, Nanjing, Jiangsu, 210095, China School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094, China su: Statistical smoothing Data mining Geographic names Long short-term memory Cities & towns Deep learning sug: subj: Statistical smoothing Data mining Geographic names Long short-term memory Cities & towns Deep learning keyword: Chinese historical newspapers digital humanities geospatial and temporal information information extraction ab: Historical newspapers are invaluable repositories of comprehensive knowledge, capturing the essence of diverse societal shifts and pivotal events across varying epochs. By scrutinizing and identifying intricate details such as place names, locations, dates, and a diverse array of Points of Interest spanning global, regional, and local scales, including countries, cities, buildings, streets, monuments, and forests, historical newspapers facilitate the reconstruction of spatial distributions and timelines of past events. This study proposes a sophisticated multi-tiered geospatial and temporal information framework. This framework is exemplified through empirical research utilizing historical newspaper texts from Chinese ' Shengjing Times Changchun Compilation '. Leveraging advanced deep learning models such as BiLSTM, BERT, and Boundary Smoothing for meticulous data annotation and extraction, the study demonstrates the feasibility and effectiveness of extracting geospatial and temporal information from historical newspaper texts. The outcomes of this research offer invaluable methodological insights and guidance for contributing significantly to the field of historical studies and information retrieval. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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