@inproceedings{caballero-etal-2025-nlpuned,
title = "{N}lp{U}ned at {S}em{E}val-2025 Task 10: Beyond Training: A Taxonomy-Guided Approach to Role Classification Using {LLM}s",
author = "Caballero, Alberto and
Rodrigo, Alvaro and
Centeno, Roberto",
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-1.42/",
pages = "296--301",
ISBN = "979-8-89176-273-2",
abstract = "The paper presents a taxonomy-guided approach to role classification in news articles using Large Language Models (LLMs). Instead of traditional model training, the system employs zero-shot and few-shot prompting strategies, leveraging structured taxonomies and contextual cues for classification. The study evaluates hierarchical and single-step classification approaches, finding that a unified, single-step model with contextual preprocessing achieves the best performance. The research underscores the importance of input structuring and classification strategy in optimizing LLM performance for real-world applications."
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%0 Conference Proceedings
%T NlpUned at SemEval-2025 Task 10: Beyond Training: A Taxonomy-Guided Approach to Role Classification Using LLMs
%A Caballero, Alberto
%A Rodrigo, Alvaro
%A Centeno, Roberto
%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 caballero-etal-2025-nlpuned
%X The paper presents a taxonomy-guided approach to role classification in news articles using Large Language Models (LLMs). Instead of traditional model training, the system employs zero-shot and few-shot prompting strategies, leveraging structured taxonomies and contextual cues for classification. The study evaluates hierarchical and single-step classification approaches, finding that a unified, single-step model with contextual preprocessing achieves the best performance. The research underscores the importance of input structuring and classification strategy in optimizing LLM performance for real-world applications.
%U https://aclanthology.org/2025.semeval-1.42/
%P 296-301
Markdown (Informal)
[NlpUned at SemEval-2025 Task 10: Beyond Training: A Taxonomy-Guided Approach to Role Classification Using LLMs](https://aclanthology.org/2025.semeval-1.42/) (Caballero et al., SemEval 2025)
ACL