@inproceedings{gemes-recski-2021-tuw,
    title = "{TUW}-{I}nf at {G}erm{E}val2021: Rule-based and Hybrid Methods for Detecting Toxic, Engaging, and Fact-Claiming Comments",
    author = "G{\'e}mes, Kinga  and
      Recski, G{\'a}bor",
    editor = "Risch, Julian  and
      Stoll, Anke  and
      Wilms, Lena  and
      Wiegand, Michael",
    booktitle = "Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments",
    month = sep,
    year = "2021",
    address = "Duesseldorf, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.germeval-1.10/",
    pages = "69--75",
    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."
}