@inproceedings{younus-qureshi-2025-nlptuducd,
    title = "nlptuducd at {S}em{E}val-2025 Task 10: Narrative Classification as a Retrieval Task through Story Embeddings",
    author = "Younus, Arjumand  and
      Qureshi, Muhammad Atif",
    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-1.228/",
    pages = "1742--1746",
    ISBN = "979-8-89176-273-2",
    abstract = "One of the most widely used elements in misinformation campaigns is media framing via certain angles which in turn implies pitching news stories through a certain narrative. Narrative twisting to align with a political agenda includes complex dynamics involving different topics, patterns and rhetoric; there is however a certain coherence with respect to the media framing agenda that is to be promoted. The shared task{'}s objective is to develop models for classifying narratives in online news from a pre-defined two-level taxonomy (Subtask 2). In this paper, we discuss the application of a Mistral 7B model, specifically E5 model, to address theSubtask two in English about finding the narrative taxonomy that a news article is trying to pitch. Our approach frames the task as a retrieval task in a similarity matching framework instead of reliance supervised learning. Our approach based on the use of a Mistral 7B model obtains a F1 on samples of 0.226 and is able to outperform the baseline provided for the competition."
}