TUW-Inf at GermEval2021: Rule-based and Hybrid Methods for Detecting Toxic, Engaging, and Fact-Claiming Comments

Kinga Gémes, Gábor Recski


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:
Bibkey:
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)
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PDF:
https://aclanthology.org/2021.germeval-1.10.pdf