UIMP-Aaman at SemEval-2025 Task11: Detecting Intensity and Emotion in Social Media and News

Aisha Aman - Parveen


Abstract
This paper presents our participation in SemEval task 11, which consists of emotion recognition in sentences written in multiple languages. We use in-context learning and fine-tuning methods to teach LLMs how to predict labels for Track A, Track B and Track C. The best results depends on track and language predicted.
Anthology ID:
2025.semeval-1.47
Volume:
Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Sara Rosenthal, Aiala Rosá, Debanjan Ghosh, Marcos Zampieri
Venues:
SemEval | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
331–335
Language:
URL:
https://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.47/
DOI:
Bibkey:
Cite (ACL):
Aisha Aman - Parveen. 2025. UIMP-Aaman at SemEval-2025 Task11: Detecting Intensity and Emotion in Social Media and News. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 331–335, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
UIMP-Aaman at SemEval-2025 Task11: Detecting Intensity and Emotion in Social Media and News (Aman - Parveen, SemEval 2025)
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PDF:
https://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.47.pdf