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            <namePart type="given">Boris</namePart>
            <namePart type="family">Velichkov</namePart>
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        <name type="personal">
            <namePart type="given">Ivelina</namePart>
            <namePart type="family">Nikolova-Koleva</namePart>
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            <namePart type="given">Milena</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Investigating Hierarchical Structure in Multi-Label Document Classification
%A Dampa, Artemis
%Y Velichkov, Boris
%Y Nikolova-Koleva, Ivelina
%Y Slavcheva, Milena
%S Proceedings of the 9th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F dampa-2025-investigating
%X 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.
%U https://aclanthology.org/2025.ranlp-stud.2/
%P 10-19

Markdown (Informal)

[Investigating Hierarchical Structure in Multi-Label Document Classification](https://aclanthology.org/2025.ranlp-stud.2/) (Dampa, RANLP 2025)

• Investigating Hierarchical Structure in Multi-Label Document Classification (Dampa, RANLP 2025)

ACL

• Artemis Dampa. 2025. Investigating Hierarchical Structure in Multi-Label Document Classification. In Proceedings of the 9th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing, pages 10–19, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.