Konferenzbeitrag
Deep learning for free indirect representation
In this paper, we present our work-inprogress to automatically identify free indirect representation (FI), a type of thought representation used in literary texts. With a deep learning approach using contextual string embeddings, we achieve f1 scores between 0.45 and 0.5 (sentence-based evaluation for the FI category) on two very different German corpora, a clear improvement on earlier attempts for this task. We show how consistently marked direct speech can help in this task. In our evaluation, we also consider human inter-annotator scores and thus address measures of certainty for this difficult phenomenon.
- Sprache
-
Englisch
- Thema
-
Deutsch
Indirekte Rede
Erlebte Rede
Automatische Sprachanalyse
Korpus <Linguistik>
Sprache
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Brunner, Annelen
Tu, Ngoc Duyen Tanja
Weimer, Lukas
Jannidis, Fotis
- Ereignis
-
Veröffentlichung
- (wer)
-
München [u.a.] : German Society for Computational Linguistics & Language Technology und Friedrich-Alexander-Universität Erlangen-Nürnberg
- (wann)
-
2019-10-15
- URN
-
urn:nbn:de:bsz:mh39-93151
- Letzte Aktualisierung
-
06.03.2025, 09:00 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Leibniz-Institut für Deutsche Sprache - Bibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Leibniz-Institut für Deutsche Sprache - Bibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Konferenzbeitrag
Beteiligte
- Brunner, Annelen
- Tu, Ngoc Duyen Tanja
- Weimer, Lukas
- Jannidis, Fotis
- München [u.a.] : German Society for Computational Linguistics & Language Technology und Friedrich-Alexander-Universität Erlangen-Nürnberg
Entstanden
- 2019-10-15
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