@inproceedings{li-etal-2025-ynu-hpcc,
    title = "{YNU}-{HPCC} at {S}em{E}val-2025 Task 10: A Two-Stage Approach to Solving Multi-Label and Multi-Class Role Classification Based on {D}e{BERT}a",
    author = "Li, Ning  and
      Zhang, You  and
      Wang, Jin  and
      Xu, Dan  and
      Zhang, Xuejie",
    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/transition-to-people-yaml/2025.semeval-1.258/",
    pages = "1993--1999",
    ISBN = "979-8-89176-273-2",
    abstract = "A two-stage role classification model based on DeBERTa is proposed for the Entity Framework task in SemEval 2025 Task 10. The task is confronted with challenges such as multi-labeling, multi-category, and category imbalance, particularly in the semantic overlap and data sparsity of fine-grained roles. Existing methods primarily rely on rules, traditional machine learning, or deep learning, but the accurate classification of fine-grained roles is difficult to achieve. To address this, the proposed model integrates the deep semantic representation of the DeBERTa pre-trained language model through two sub-models: main role classification and sub-role classification, and utilizes Focal Loss to optimize the category imbalance issue. Experimental results indicate that the model achieves an accuracy of 75.32{\%} in predicting the main role, while the exact matching rate for the sub-role is 8.94{\%}. This is mainly limited by the strict matching standard and semantic overlap of fine-grained roles in the multi-label task. Compared to the baseline{'}s sub-role exact matching rate of 3.83{\%}, the proposed model significantly improves this metric. The model ultimately ranked 23rd on the leaderboard. The code of this paper is available at:https://github.com/jiyuaner/YNU-HPCC-at-SemEval-2025-Task10."
}