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.
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| Author: | Annelen BrunnerGND, Ngoc Duyen Tanja TuORCiDGND, Lukas Weimer, Fotis Jannidis |
|---|---|
| URN: | urn:nbn:de:bsz:mh39-93151 |
| URL: | https://corpora.linguistik.uni-erlangen.de/data/konvens/proceedings/papers/KONVENS2019_paper_27.pdf |
| Parent Title (English): | Preliminary proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019), October 9 – 11, 2019 at Friedrich-Alexander-Universität Erlangen-Nürnberg |
| Publisher: | German Society for Computational Linguistics & Language Technology und Friedrich-Alexander-Universität Erlangen-Nürnberg |
| Place of publication: | München [u.a.] |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2019 |
| Date of Publication (online): | 2019/10/15 |
| Publicationstate: | Veröffentlichungsversion |
| Reviewstate: | Peer-Review |
| GND Keyword: | Automatische Sprachanalyse; Deutsch; Erlebte Rede; Indirekte Rede; Korpus <Linguistik> |
| First Page: | 241 |
| Last Page: | 245 |
| DDC classes: | 400 Sprache / 400 Sprache, Linguistik |
| Open Access?: | ja |
| Leibniz-Classification: | Sprache, Linguistik |
| Linguistics-Classification: | Computerlinguistik |
| Program areas: | Lexik |
| Licence (German): |  Creative Commons - CC BY-NC-SA - Namensnennung - Nicht kommerziell - Weitergabe unter gleichen Bedingungen 4.0 International |