TU Darmstadt / ULB / TUprints

The (In-)Consistency of Literary Concepts. Operationalising, Annotating and Detecting Literary Comment

Weimer, Anna Mareike ; Barth, Florian ; Dönicke, Tillmann ; Gödeke, Luisa ; Varachkina, Hanna ; Holler, Anke ; Sporleder, Caroline ; Gittel, Benjamin (2024)
The (In-)Consistency of Literary Concepts. Operationalising, Annotating and Detecting Literary Comment.
In: Journal of Computational Literary Studies, 2022, 1 (1)
doi: 10.26083/tuprints-00026619
Article, Secondary publication, Publisher's Version

This is the latest version of this item.

[img] Text
jcls-90-weimer.pdf
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (225kB)
[img] Text
jcls-90-weimer.xml
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (162kB)
Item Type: Article
Type of entry: Secondary publication
Title: The (In-)Consistency of Literary Concepts. Operationalising, Annotating and Detecting Literary Comment
Language: English
Date: 5 February 2024
Place of Publication: Darmstadt
Year of primary publication: 2022
Journal or Publication Title: Journal of Computational Literary Studies
Volume of the journal: 1
Issue Number: 1
Collation: 25 Seiten
DOI: 10.26083/tuprints-00026619
Corresponding Links:
Origin: Secondary publication from TUjournals
Abstract:

This paper explores how both annotation procedures and automatic detection (i.e. classifiers) can be used to assess the consistency of textual literary concepts. We developed an annotation tagset for the ‘literary comment’ – a frequently used but rarely defined concept – and its subtypes (interpretative comment, attitude comment and metanarrative/metafictional comment) and trained a multi-output and a binary classifier. The multi-output classifier shows F-scores of 28% for attitude comment, 36% for interpretative comment and 48% for meta comment, whereas the binary classifier achieves F-scores up to 59%. Crucially, both our annotation and the automatic classification struggle with the same subtypes of comment, although annotation and classification follow completely different procedures. Our findings suggest an inconsistency in the overall literary concept ‘comment’ and most prominently the subtypes ‘attitude comment’ and ‘interpretative comment’. As a best-practice-example, our approach illustrates that the contribution of Digital Humanities to Literary Studies may go beyond the automatic recognition of literary phenomena.

Uncontrolled Keywords: literary theory, narratology, commentary, operationalisation, annotation, supervised machine learning
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-266191
Additional Information:

Urspr. Konferenzveröffentlichung/Originally conference publication: 1st Annual Conference of Computational Literary Studies, 01.-02.06.2022, Darmstadt, Germany

Classification DDC: 800 Literature > 800 Literature, rhetoric and criticism
Divisions: 02 Department of History and Social Science > Institut für Sprach- und Literaturwissenschaft > Digital Philology – Modern German Literary Studies
Date Deposited: 05 Feb 2024 13:05
Last Modified: 22 Jul 2024 08:09
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/26619
PPN:
Export:

Available Versions of this Item

Actions (login required)
View Item View Item