Abstract
Supervised disambiguation of verbal idioms (VID) poses special demands on the quality and quantity of the annotated data used for learning and evaluation. In this paper, we present a new VID corpus for German and perform a series of VID disambiguation experiments on it. Our best classifier, based on a neural architecture, yields an error reduction across VIDs of 57% in terms of accuracy compared to a simple majority baseline.- Anthology ID:
- 2020.figlang-1.29
- Volume:
- Proceedings of the Second Workshop on Figurative Language Processing
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Beata Beigman Klebanov, Ekaterina Shutova, Patricia Lichtenstein, Smaranda Muresan, Chee Wee, Anna Feldman, Debanjan Ghosh
- Venue:
- Fig-Lang
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 211–220
- Language:
- URL:
- https://aclanthology.org/2020.figlang-1.29
- DOI:
- 10.18653/v1/2020.figlang-1.29
- Cite (ACL):
- Rafael Ehren, Timm Lichte, Laura Kallmeyer, and Jakub Waszczuk. 2020. Supervised Disambiguation of German Verbal Idioms with a BiLSTM Architecture. In Proceedings of the Second Workshop on Figurative Language Processing, pages 211–220, Online. Association for Computational Linguistics.
- Cite (Informal):
- Supervised Disambiguation of German Verbal Idioms with a BiLSTM Architecture (Ehren et al., Fig-Lang 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.figlang-1.29.pdf