@inproceedings{ermurachi-gifu-2020-uaic1860,
    title = "{UAIC}1860 at {S}em{E}val-2020 Task 11: Detection of Propaganda Techniques in News Articles",
    author = "Ermurachi, Vlad  and
      Gifu, Daniela",
    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/ingest-emnlp/2020.semeval-1.241/",
    doi = "10.18653/v1/2020.semeval-1.241",
    pages = "1835--1840",
    abstract = "The ``Detection of Propaganda Techniques in News Articles'' task at the SemEval 2020 competition focuses on detecting and classifying propaganda, pervasive in news article. In this paper, we present a system able to evaluate on sentence level, three traditional text representation techniques for these study goals, using: tf*idf, word and character n-grams. Firstly, we built a binary classifier able to provide corresponding propaganda labels, propaganda or non-propaganda. Secondly, we build a multilabel multiclass model to identify applied propaganda."
}