Trained on fictional and non-fictional German texts written between 1840 and 1920:
- Narrative texts from Digitale Bibliothek (https://textgrid.de/digitale-bibliothek)
- Fairy tales and sagas from Grimm Korpus (https://www1.ids-mannheim.de/kl/projekte/korpora/archiv/gri.html)
- Newspaper and magazine article from Mannheimer Korpus Historischer Zeitungen und Zeitschriften (https://repos.ids-mannheim.de/mkhz-beschreibung.html)
- Magazine article from the journal „Die Grenzboten“ (http://www.deutschestextarchiv.de/doku/textquellen#grenzboten)
- Fictional and non-fictional texts from Projekt Gutenberg (https://www.projekt-gutenberg.org)
1 Tesla P4 GPU
Evaluation results: Automatic tagging of four forms of speech/thought/writing representation in historical fictional and non-fictional German texts
The language model was used in the task to tag direct, indirect, reported and free indirect speech/thought/writing representation in fictional and non-fictional German texts. The tagger is available and described in detail at https://github.com/redewiedergabe/tagger.
The tagging model was trained using the SequenceTagger Class of the Flair framework (Akbik et al., 2019) which implements a BiLSTM-CRF architecture on top of a language embedding (as proposed by Huang et al. (2015)).
Results are reported below in comparison to a custom trained flair embedding, which was stacked onto a custom trained fastText-model. Both models were trained on the same dataset.
|Direct||0.80||0.86||0.74||0.84||0.90||0.79||historical German, fictional & non-fictional|
|Indirect||0.76||0.79||0.73||0.73||0.78||0.68||historical German, fictional & non-fictional|
|Reported||0.58||0.69||0.51||0.56||0.68||0.48||historical German, fictional & non-fictional|
|Free indirect||0.57||0.80||0.44||0.47||0.78||0.34||modern German, fictional|
Historical German Texts (1840 to 1920)
(Showed good performance with modern German fictional texts as well)
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