Abstract
This paper describes our methods submitted for the GermEval 2021 shared task on identifying toxic, engaging and fact-claiming comments in social media texts (Risch et al., 2021). We explore simple strategies for semi-automatic generation of rule-based systems with high precision and low recall, and use them to achieve slight overall improvements over a standard BERT-based classifier.- Anthology ID:
- 2021.germeval-1.10
- Volume:
- Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments
- Month:
- September
- Year:
- 2021
- Address:
- Duesseldorf, Germany
- Editors:
- Julian Risch, Anke Stoll, Lena Wilms, Michael Wiegand
- Venue:
- GermEval
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 69–75
- Language:
- URL:
- https://aclanthology.org/2021.germeval-1.10
- DOI:
- Cite (ACL):
- Kinga Gémes and Gábor Recski. 2021. TUW-Inf at GermEval2021: Rule-based and Hybrid Methods for Detecting Toxic, Engaging, and Fact-Claiming Comments. In Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments, pages 69–75, Duesseldorf, Germany. Association for Computational Linguistics.
- Cite (Informal):
- TUW-Inf at GermEval2021: Rule-based and Hybrid Methods for Detecting Toxic, Engaging, and Fact-Claiming Comments (Gémes & Recski, GermEval 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.germeval-1.10.pdf