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

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.