@inproceedings{wang-etal-2025-team-insantive,
    title = "Team {INSA}ntive at {S}em{E}val-2025 Task 10: Hierarchical Text Classification using {BERT}",
    author = "Wang, Yutong  and
      Nurbakova, Diana  and
      Calabretto, Sylvie",
    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-1.130/",
    pages = "981--988",
    ISBN = "979-8-89176-273-2",
    abstract = "In this paper, we propose a BERT-based hierarchical text classification framework to address the challenges of training multi-level classification tasks. As part of the SemEval-2025 Task 10 challenge (Subtask 2), the framework performs fine-grained text classification by training dedicated sub-category classifiers for each top-level category. Experimental results demonstrate the feasibility of the proposed approach in multi-class text classification tasks."
}