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Modeling and Measuring Short Text Similarities. On the Multi-Dimensional Differences between German Poetry of Realism and Modernism

Ehrmanntraut, Anton ; Hagen, Thora ; Jannidis, Fotis ; Konle, Leonard ; Kröncke, Merten ; Winko, Simone (2023)
Modeling and Measuring Short Text Similarities. On the Multi-Dimensional Differences between German Poetry of Realism and Modernism.
In: Journal of Computational Literary Studies, 2022, 1 (1)
doi: 10.26083/tuprints-00023259
Artikel, Zweitveröffentlichung, Verlagsversion

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Kurzbeschreibung (Abstract)

This study contributes to the ongoing discussion on how to operationalize text similarity for the purposes of computational literary studies by defining, justifying theoretically and employing a multi-dimensional text model. Additionally, we evaluate a set of strategies to implement this model for very short texts like poetry using a range of methods from weighted sparse vectors up to very recent neural sentence embeddings based on annotations of emotions, genre and similarity. And finally, we show the relevance of using such a complex text model by applying the best method to a research question about the development of early modernism in German poetry. While we can confirm some important hypotheses from literary studies, we are also able to differentiate or relativize others. In particular, our findings do not support the widely held thesis that the change from realism to modernism was a revolutionary ‘rupture’.

Typ des Eintrags: Artikel
Erschienen: 2023
Autor(en): Ehrmanntraut, Anton ; Hagen, Thora ; Jannidis, Fotis ; Konle, Leonard ; Kröncke, Merten ; Winko, Simone
Art des Eintrags: Zweitveröffentlichung
Titel: Modeling and Measuring Short Text Similarities. On the Multi-Dimensional Differences between German Poetry of Realism and Modernism
Sprache: Englisch
Publikationsjahr: 2023
Ort: Darmstadt
Publikationsdatum der Erstveröffentlichung: 2022
Verlag: Universitäts- und Landesbibliothek Darmstadt
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Journal of Computational Literary Studies
Jahrgang/Volume einer Zeitschrift: 1
(Heft-)Nummer: 1
Kollation: 30 Seiten
DOI: 10.26083/tuprints-00023259
URL / URN: https://tuprints.ulb.tu-darmstadt.de/23259
Zugehörige Links:
Herkunft: Zweitveröffentlichung von TUjournals
Kurzbeschreibung (Abstract):

This study contributes to the ongoing discussion on how to operationalize text similarity for the purposes of computational literary studies by defining, justifying theoretically and employing a multi-dimensional text model. Additionally, we evaluate a set of strategies to implement this model for very short texts like poetry using a range of methods from weighted sparse vectors up to very recent neural sentence embeddings based on annotations of emotions, genre and similarity. And finally, we show the relevance of using such a complex text model by applying the best method to a research question about the development of early modernism in German poetry. While we can confirm some important hypotheses from literary studies, we are also able to differentiate or relativize others. In particular, our findings do not support the widely held thesis that the change from realism to modernism was a revolutionary ‘rupture’.

Freie Schlagworte: short text, similarity, poetry, modernism, realism
Status: Verlagsversion
URN: urn:nbn:de:tuda-tuprints-232599
Zusätzliche Informationen:

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

Sachgruppe der Dewey Dezimalklassifikatin (DDC): 800 Literatur > 800 Literatur, Rhetorik, Literaturwissenschaft
Fachbereich(e)/-gebiet(e): 02 Fachbereich Gesellschafts- und Geschichtswissenschaften > Institut für Sprach- und Literaturwissenschaft > Digital Philology - Neuere deutsche Literaturwissenschaft
02 Fachbereich Gesellschafts- und Geschichtswissenschaften
02 Fachbereich Gesellschafts- und Geschichtswissenschaften > Institut für Sprach- und Literaturwissenschaft
Hinterlegungsdatum: 21 Feb 2023 10:28
Letzte Änderung: 27 Feb 2023 12:47
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