@inproceedings{bruckner-pecina-2025-hierarchical,
title = "Hierarchical Classification of Propaganda Techniques in {S}lavic Texts in Hyperbolic Space",
author = {Br{\"u}ckner, Christopher and
Pecina, Pavel},
editor = "Piskorski, Jakub and
P{\v{r}}ib{\'a}{\v{n}}, Pavel and
Nakov, Preslav and
Yangarber, Roman and
Marcinczuk, Michal",
booktitle = "Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.bsnlp-1.21/",
doi = "10.18653/v1/2025.bsnlp-1.21",
pages = "183--189",
ISBN = "978-1-959429-57-9",
abstract = "Classification problems can often be tackled by modeling label hierarchies with broader categories in a graph and solving the task via node classification. While recent advances have shown that hyperbolic space is more suitable than Euclidean space for learning graph representations, this concept has yet to be applied to text classification, where node features first need to be extracted from text embeddings. A prototype of such an architecture is this contribution to the Slavic NLP 2025 shared task on the multi-label classification of persuasion techniques in parliamentary debates and social media posts. We do not achieve state-of-the-art performance, but outline the benefits of this hierarchical node classification approach and the advantages of hyperbolic graph embeddings"
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<abstract>Classification problems can often be tackled by modeling label hierarchies with broader categories in a graph and solving the task via node classification. While recent advances have shown that hyperbolic space is more suitable than Euclidean space for learning graph representations, this concept has yet to be applied to text classification, where node features first need to be extracted from text embeddings. A prototype of such an architecture is this contribution to the Slavic NLP 2025 shared task on the multi-label classification of persuasion techniques in parliamentary debates and social media posts. We do not achieve state-of-the-art performance, but outline the benefits of this hierarchical node classification approach and the advantages of hyperbolic graph embeddings</abstract>
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%0 Conference Proceedings
%T Hierarchical Classification of Propaganda Techniques in Slavic Texts in Hyperbolic Space
%A Brückner, Christopher
%A Pecina, Pavel
%Y Piskorski, Jakub
%Y Přibáň, Pavel
%Y Nakov, Preslav
%Y Yangarber, Roman
%Y Marcinczuk, Michal
%S Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 978-1-959429-57-9
%F bruckner-pecina-2025-hierarchical
%X Classification problems can often be tackled by modeling label hierarchies with broader categories in a graph and solving the task via node classification. While recent advances have shown that hyperbolic space is more suitable than Euclidean space for learning graph representations, this concept has yet to be applied to text classification, where node features first need to be extracted from text embeddings. A prototype of such an architecture is this contribution to the Slavic NLP 2025 shared task on the multi-label classification of persuasion techniques in parliamentary debates and social media posts. We do not achieve state-of-the-art performance, but outline the benefits of this hierarchical node classification approach and the advantages of hyperbolic graph embeddings
%R 10.18653/v1/2025.bsnlp-1.21
%U https://aclanthology.org/2025.bsnlp-1.21/
%U https://doi.org/10.18653/v1/2025.bsnlp-1.21
%P 183-189
Markdown (Informal)
[Hierarchical Classification of Propaganda Techniques in Slavic Texts in Hyperbolic Space](https://aclanthology.org/2025.bsnlp-1.21/) (Brückner & Pecina, BSNLP 2025)
ACL