Using deep learning to analyse the times of the UN Security Council.
This article analyses how digital humanities scholarship can make use of recent advances in deep learning to analyse the temporal relations in an online textual archive. We use transfer learning as well as data augmentation techniques to investigate changes in United Nations Security Council resolut...
| Publicado en: | Digital Scholarship in the Humanities Vol. 39; no. 2; pp. 485 - 500 |
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| Formato: | Artículo |
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Oxford University Press / USA
Jun2024
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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=177947254&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 177947254 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Jun2024 vid: 39 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 177947254 10.1093/llc/fqae009 ppf: 485 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.1MB tig: atl: Using deep learning to analyse the times of the UN Security Council. aug: au: Blanke, Tobias affil: Institute for Logic, Language and Computation, University of Amsterdam , Science Park 107 , 1098 XG Amsterdam, Netherlands su: United Nations. Security Council Deep learning Data augmentation Digital humanities sug: subj: United Nations. Security Council Deep learning Data augmentation Digital humanities keyword: explainability text regression transfer learning United Nations Security Council ab: This article analyses how digital humanities scholarship can make use of recent advances in deep learning to analyse the temporal relations in an online textual archive. We use transfer learning as well as data augmentation techniques to investigate changes in United Nations Security Council resolutions. Instead of pre-defined periods, as it is common, we target the years directly. Such a text regression task is novel in the digital humanities as far as we can see and has the advantage of speaking directly to historical relations. We present not only very good experimental results but also demonstrate how such text regressions can be interpreted directly and with surrogate topic models. 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: 2024 holdings: @attributes: islocal: N |
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