Automated Compositional Change Detection in Saxo Grammaticus’ Gesta Danorum

Kristoffer Laigaard Nielbo, Mads Linnet Perner, Christan Larsen, Jonas Nielsen, Ditte Laursen

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

Abstract

Saxo Grammaticus’ medieval source Gesta Danorum (“Deeds of the Danes”) represents the beginning of the modern historical research in Denmark. The bipartite composition of Gesta Danorum has however been subject to much academic debate. In particular the nature and location of a transition between early Pre-Christian and late Christian content have given rise to two competing accounts. In this paper, we argue that the debate can be represented as a problem of intratextual dynamics and we combine models from Information Retrieval and Natural Language Processing with techniques for time series analysis in order to reevaluate the debate. Results indicate that the transition is gradual, starting in book eight and ending in book ten, but that a point-like interpretation is possible in book nine. We argue that the approach exemplifies scalable“automated close reading”, which has multiple applications in text-based historical research.
OriginalsprogEngelsk
TitelDHN 2019 Digital Humanities in the Nordic Countries : Proceedings of the Digital Humanities in the Nordic Countries 4th Conference
RedaktørerCostanza Navarretta, Manex Agirrezabal, Bente Maegaard
Antal sider13
Vol/bind2364
ForlagCEUR-WS.org
Publikationsdato2019
Sider320-332
StatusUdgivet - 2019
Udgivet eksterntJa
Begivenhed4th Digital Humanities in the Nordic Countries - Faculty of Humanities - University of Copenhagen, Copenhagen, Danmark
Varighed: 6 mar. 20198 mar. 2019
Konferencens nummer: 4
https://cst.dk/DHN2019/DHN2019.html

Konference

Konference4th Digital Humanities in the Nordic Countries
Nummer4
LokationFaculty of Humanities - University of Copenhagen
LandDanmark
ByCopenhagen
Periode06/03/201908/03/2019
Internetadresse
NavnCEUR Workshop Proceedings
Nummer2364

Emneord

  • Change Detection
  • Cultural Heritage
  • Medieval Literature
  • Text Analysis
  • Topic Modeling

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