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

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

Authors: 
Kinga Andrea Gemes
Gábor Recski
Type: 
Proceedings contribution
Proceedings: 
Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021
Publisher: 
netlibrary
Pages: 
69 - 75
ISBN: 
Year: 
2021
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. 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.
TU Focus: 
Computational Science and Engineering
Reference: 

K. A. Gemes, G. Recski:
"TUW-Inf at GermEval2021: Rule-based and Hybrid Methods for Detecting Toxic, Engaging, and Fact-Claiming Comments";
in: "Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021", netlibrary, 2021, S. 69 - 75.

Zusätzliche Informationen

PDF Link: 
https://netlibrary.aau.at/obvukloa/content/pageview/6435282
Last changed: 
08.09.2021 16:17:27
TU Id: 
297063
Accepted: 
Accepted
Invited: 
Department Focus: 
Business Informatics
Info Link: 
https://publik.tuwien.ac.at/showentry.php?ID=297063&lang=1
Abstract German: 
Author List: 
K. A. Gemes, G. Recski