Smart Modelling for Literary History.
MiMoText is a research project in computational literary studies dealing with new ways to model and analyse literary history and literary historiography. It is based on the idea of extracting statements relevant to literary history from bibliographies, scholarly publications and primary sources, in...
| Publicado en: | International Journal of Humanities & Arts Computing: A Journal of Digital Humanities Vol. 16; no. 1; pp. 78 - 94 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
Edinburgh University Press
Mar2022
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| Materias: | |
| 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=155907001&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 155907001 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 17538548 2QD7 jtl: International Journal of Humanities & Arts Computing: A Journal of Digital Humanities issn: 17538548 maglogo: N pubinfo: dt: Mar2022 vid: 16 iid: 1 pid: 2327 pub: Edinburgh University Press artinfo: ui: 155907001 10.3366/ijhac.2022.0278 ppf: 78 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.2MB tig: atl: Smart Modelling for Literary History. aug: au: Schöch, Christof Hinzmann, Maria Röttgermann, Julia Dietz, Katharina Klee, Anne su: Linked data (Semantic Web) Data mining Text mining Digital humanities Literary criticism sug: subj: Linked data (Semantic Web) Data mining Text mining Digital humanities Literary criticism keyword: computational literary studies data modelling information extraction Linked Open Data literary history ab: MiMoText is a research project in computational literary studies dealing with new ways to model and analyse literary history and literary historiography. It is based on the idea of extracting statements relevant to literary history from bibliographies, scholarly publications and primary sources, in order to build a shared knowledge network for literary history. We employ methods from information extraction and text mining to obtain large numbers of statements about authors and literary works from our data. Moreover, we use the Linked Open Data paradigm to model, represent and query the information we obtain. We believe our project is a step towards a mode of digital humanities that goes not only beyond small, deeply encoded datasets and their close reading, but also beyond Big Data approaches that cannot always be easily adapted to the humanities. Instead, we propose a third way for digital humanities that develops quantitative methods to create and analyse datasets relevant to research in the humanities that are both larger and smarter than has been customary up until recently. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of International Journal of Humanities & Arts Computing: A Journal of Digital Humanities is the property of Edinburgh University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: International Journal of Humanities & Arts Computing: A Journal of Digital Humanities holder: Edinburgh University Press dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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