Toward a computational history of universities: Evaluating text mining methods for interdisciplinarity detection from PhD dissertation abstracts.
For the first time, historians of higher education have large data sets of primary sources that reflect the complete output of academic institutions at their disposal. To analyze this unprecedented abundance of digital materials, scholars have access to a large suite of computational methods develop...
| Publicado en: | Digital Scholarship in the Humanities Vol. 33; no. 3; pp. 612 - 621 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
Sep2018
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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=131417031&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 131417031 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Sep2018 vid: 33 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 131417031 10.1093/llc/fqx062 ppf: 612 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P size: 313KB tig: atl: Toward a computational history of universities: Evaluating text mining methods for interdisciplinarity detection from PhD dissertation abstracts. aug: au: Nanni, Federico Dietz, Laura Ponzetto, Simone Paolo affil: International Centre for the History of Universities and Science, University of Bologna and Data and Web Science Group, University of Mannheim Department of Computer Science, University of New Hampshire, USA Data and Web Science Group, University of Mannheim su: Text mining Interdisciplinary education Academic dissertations Natural language processing Big data sug: subj: Text mining Interdisciplinary education Academic dissertations Natural language processing Big data ab: For the first time, historians of higher education have large data sets of primary sources that reflect the complete output of academic institutions at their disposal. To analyze this unprecedented abundance of digital materials, scholars have access to a large suite of computational methods developed in the field of Natural Language Processing. However, when the intention is to move beyond exploratory studies and use the results of such analyses as quantitative evidences, historians need to take into account the reliability of these techniques. The main goal of this article is to investigate the performance of different text mining methods for a specific task: the automatic identification of interdisciplinary works from a corpus of PhD dissertation abstracts. Based on the output of our study, we provide the research community of a new data set for analyzing recent changes in interdisciplinary practices in a large sample of European universities. We show the potential of this collection by tracking the growth in adoption of computational approaches across different research fields, during the past 30 years. 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: 2018 holdings: @attributes: islocal: N |
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