@inproceedings{baumann-deisenhofer-2023-framingfreaks,
    title = "{F}raming{F}reaks at {S}em{E}val-2023 Task 3: Detecting the Category and the Framing of Texts as Subword Units with Traditional Machine Learning",
    author = "Baumann, Rosina  and
      Deisenhofer, Sabrina",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.semeval-1.127/",
    doi = "10.18653/v1/2023.semeval-1.127",
    pages = "922--926",
    abstract = "This paper describes our participation as team FramingFreaks in the SemEval-2023 task 3 {\textquotedblleft}Category and Framing Predictions in online news in a multi-lingual setup.{\textquotedblright} We participated in subtasks 1 and 2. Our approach was to classify texts by splitting them into subwords to reduce the feature set size and then using these tokens as input in Support Vector Machine (SVM) or logistic regression classifiers. Our results are similar to the baseline results."
}