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.

Urheber*in: Brunner, Annelen; Tu, Ngoc Duyen Tanja; Weimer, Lukas; Jannidis, Fotis

Attribution - NonCommercial - ShareAlike 4.0 International

Language: Englisch

Subject: Deutsch
Indirekte Rede
Erlebte Rede
Automatische Sprachanalyse
Korpus <Linguistik>
Sprache

Event: Geistige Schöpfung

(who): Brunner, Annelen
Tu, Ngoc Duyen Tanja
Weimer, Lukas
Jannidis, Fotis

Event: Veröffentlichung

(who): München [u.a.] : German Society for Computational Linguistics & Language Technology und Friedrich-Alexander-Universität Erlangen-Nürnberg

(when): 2019-10-15

URN: urn:nbn:de:bsz:mh39-93151

Last update: 06.03.2025, 9:00 AM CET

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Konferenzbeitrag

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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

Time of origin

2019-10-15

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Deep learning for free indirect representation

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