@inproceedings{fraile-hernandez-penas-2025-unedteam,
title = "{UNEDT}eam at {S}em{E}val-2025 Task 10: Zero-Shot Narrative Classification",
author = "Fraile - Hernandez, Jesus M. and
Pe{\~n}as, Anselmo",
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://aclanthology.org/2025.semeval-1.24/",
pages = "165--173",
ISBN = "979-8-89176-273-2",
abstract = "In this paper we present our participation in Subtask 2 of SemEval-2025 Task 10, focusing on the identification and classification of narratives in news of multiple languages, on climate change and the Ukraine-Russia war. To address this task, we employed a Zero-Shot approach using a generative Large Language Model without prior training on the dataset. Our classification strategy is based on two steps: first, the system classifies the topic of each news item; subsequently, it identifies the sub-narratives directly at the finer granularity. We present a detailed analysis of the performance of our system compared to the best ranked systems on the leaderboard, highlighting the strengths and limitations of our approach."
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<abstract>In this paper we present our participation in Subtask 2 of SemEval-2025 Task 10, focusing on the identification and classification of narratives in news of multiple languages, on climate change and the Ukraine-Russia war. To address this task, we employed a Zero-Shot approach using a generative Large Language Model without prior training on the dataset. Our classification strategy is based on two steps: first, the system classifies the topic of each news item; subsequently, it identifies the sub-narratives directly at the finer granularity. We present a detailed analysis of the performance of our system compared to the best ranked systems on the leaderboard, highlighting the strengths and limitations of our approach.</abstract>
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%0 Conference Proceedings
%T UNEDTeam at SemEval-2025 Task 10: Zero-Shot Narrative Classification
%A Fraile - Hernandez, Jesus M.
%A Peñas, Anselmo
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F fraile-hernandez-penas-2025-unedteam
%X In this paper we present our participation in Subtask 2 of SemEval-2025 Task 10, focusing on the identification and classification of narratives in news of multiple languages, on climate change and the Ukraine-Russia war. To address this task, we employed a Zero-Shot approach using a generative Large Language Model without prior training on the dataset. Our classification strategy is based on two steps: first, the system classifies the topic of each news item; subsequently, it identifies the sub-narratives directly at the finer granularity. We present a detailed analysis of the performance of our system compared to the best ranked systems on the leaderboard, highlighting the strengths and limitations of our approach.
%U https://aclanthology.org/2025.semeval-1.24/
%P 165-173
Markdown (Informal)
[UNEDTeam at SemEval-2025 Task 10: Zero-Shot Narrative Classification](https://aclanthology.org/2025.semeval-1.24/) (Fraile - Hernandez & Peñas, SemEval 2025)
ACL