@inproceedings{bairaktaris-etal-2020-duth,
    title = "{DUTH} at {S}em{E}val-2020 Task 11: {BERT} with Entity Mapping for Propaganda Classification",
    author = "Bairaktaris, Anastasios  and
      Symeonidis, Symeon  and
      Arampatzis, Avi",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.227/",
    doi = "10.18653/v1/2020.semeval-1.227",
    pages = "1732--1738",
    abstract = "This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. Our team dealt with Subtask 2: Technique Classification. We used shallow Natural Language Processing (NLP) preprocessing techniques to reduce the noise in the dataset, feature selection methods, and common supervised machine learning algorithms. Our final model is based on using the BERT system with entity mapping. To improve our model`s accuracy, we mapped certain words into five distinct categories by employing word-classes and entity recognition"
}