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Annotating and Classifying Direct Speech in Historical Danish and Norwegian Literary Texts

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Abstract

Analyzing direct speech in historical literary texts provides insights into character dynamics, narrative style, and discourse patterns. In late 19th century Danish and Norwegian fiction direct speech
reflects characters’ social and geographical backgrounds. However, inconsistent
typographic conventions in Scandinavian
literature complicate computational methods for distinguishing direct speech from
other narrative elements. To address this,
we introduce an annotated dataset from the
MeMo corpus, capturing speech markers
and tags in Danish and Norwegian novels.
We evaluate pre-trained language models
for classifying direct speech, with results
showing that a Danish Foundation Model
(DFM), trained on extensive Danish data,
has the highest performance. Finally, we
conduct a classifier-assisted quantitative
corpus analysis and find a downward trend
in the prevalence of speech over time.
Original languageEnglish
Title of host publicationProceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025
EditorsSara Stymne, Richard Johansson
PublisherUniversity of Tartu Library
Publication date2025
Pages1-7
ISBN (Electronic)978-9908-53-109-0
DOIs
Publication statusPublished - 2025
Event25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies - Tallinn, Estonia
Duration: 3 Mar 20254 Mar 2025

Conference

Conference25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies
Country/TerritoryEstonia
CityTallinn
Period03/03/202504/03/2025
SeriesNEALT (Northern European Association of Language Technology) Proceedings Series
Volume57
ISSN1736-6305

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