@inproceedings{wang-etal-2025-gowithnlp,
    title = "gowithnlp at {S}em{E}val-2025 Task 10: Leveraging Entity-Centric Chain of Thought and Iterative Prompt Refinement for Multi-Label Classification",
    author = "Wang, Bo  and
      Song, Ruichen  and
      Wang, Xiangyu  and
      Shi, Ge  and
      Hu, Linmei  and
      Huang, Heyan  and
      Feng, Chong",
    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-1.53/",
    pages = "373--380",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our system for Subtask 10 of Entity Framing, which focuses on assigning one or more hierarchical roles to named entities in news articles. Our approach iteratively refines prompts and utilizes the Entity-Centric Chain of Thought to complete the task. Specifically, to minimize ambiguity in label definitions, we use the model{'}s predictions as supervisory signals, iteratively refining the category definitions. Furthermore, to minimize the interference of irrelevant information during inference, we incorporate entity-related information into the CoT framework, allowing the model to focus more effectively on entity-centric reasoning. Our system achieved the highest ranking on the leaderboard in the Russian main role classification and the second in English, with an accuracy of 0.8645 and 0.9362, respectively. We discuss the impact of several components of our multilingual classification approach, highlighting their effectiveness."
}