@inproceedings{tan-etal-2025-ynuzwt,
    title = "{YNU}zwt at {S}em{E}val-2025 Task 10: Tree-guided Stagewise Classifier for Entity Framing and Narrative Classification",
    author = "Tan, Qiangyu  and
      Cui, Yuhang  and
      Tang, Zhiwen",
    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-1.277/",
    pages = "2133--2139",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents a hierarchical classification framework, designated as the Tree-guided Stagewise Classifier (TGSC) , which implements a Chain-of-Thought (CoT) reasoning paradigm for addressing multi-label and multi-class classification challenges in multilingual news article analysis in SemEval-2025 Task 10. The proposed methodology leverages the zero-shot capabilities inherent in Large Language Models (LLMs) through a systematic hierarchical reasoning mechanism. This process proceeds through successive hierarchical levels, wherein the classification commences from root nodes and progressively navigates category branches via iterative determinations at each hierarchical tier, ultimately culminating in leaf category identification during the final classification stage. To optimize classification precision, a specialized prompt engineering strategy incorporating hierarchical structural constraints is developed to guide the reasoning trajectory. Experimental results demonstrate the effectiveness of our approach, achieving competitive performance across multiple languages in Subtask 1 and Subtask 2."
}