@inproceedings{karabulut-etal-2025-tuebingen,
    title = "Tuebingen at {S}em{E}val-2025 Task 10: Class Weighting, External Knowledge and Data Augmentation in {BERT} Models",
    author = {Karabulut, {\"O}zlem  and
      Eslami, Soudabeh  and
      Gharaee, Ali  and
      Andrews, Matthew},
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.166/",
    pages = "1248--1254",
    ISBN = "979-8-89176-273-2",
    abstract = "The spread of disinformation and propaganda in online news presents a significant challengeto information integrity. As part of the SemEval 2025 Task-10 on Multilingual Characterization and Extraction of Narratives from Online News, this study focuses on Subtask 1: Entity Framing, which involves assigning roles to named entities within news articles across multiple languages.We investigate techniques such as data augmentation, external knowledge, and class weighting to improve classification performance. Our findings indicate that class weighting was more effective than other approaches"
}