@inproceedings{dampa-2025-investigating,
    title = "Investigating Hierarchical Structure in Multi-Label Document Classification",
    author = "Dampa, Artemis",
    editor = "Velichkov, Boris  and
      Nikolova-Koleva, Ivelina  and
      Slavcheva, Milena",
    booktitle = "Proceedings of the 9th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-stud.2/",
    pages = "10--19",
    abstract = "Effectively organizing the vast and ever-growing body of research in scientific literature is crucial to advancing the field and supporting scholarly discovery. In this paper, we study the task of fine-grained hierarchical multi-label classification of scholarly articles, using a structured taxonomy. Specifically, we investigate whether incorporating hierarchical information in a classification method can improve performance compared to conventional flat classification approaches. To this end, we suggest and evaluate different strategies for the classification, on three different axes: selection of positive and negative samples; soft-to-hard label mapping; hierarchical post-processing policies that utilize taxonomy-related requirements to update the final labeling. Experiments demonstrate that flat baselines constitute powerful baselines, but the infusion of hierarchical knowledge leads to better recall-focused performance based on use-case requirements."
}