@inproceedings{creanga-etal-2025-team-unibuc,
title = "Team {U}nibuc - {NLP} at {S}em{E}val-2025 Task 11: Few-shot text-based emotion detection",
author = "Creanga, Claudiu and
Marchitan, Teodor - George and
Dinu, Liviu",
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-1.65/",
pages = "468--475",
ISBN = "979-8-89176-273-2",
abstract = "This paper describes the approach of the Unibuc - NLP team in tackling the SemEval 2025 Workshop, Task 11: Bridging the Gap in Text-Based Emotion Detection. We mainly focused on experiments using large language models (Gemini, Qwen, DeepSeek) with either few-shot prompting or fine-tuning. Withour final system, for the multi-label emotion detection track (track A), we got an F1-macro of 0.7546 (26/96 teams) for the English subset, 0.1727 (35/36 teams) for the Portuguese (Mozambican) subset and 0.325 (1/31 teams) for the Emakhuwa subset."
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<abstract>This paper describes the approach of the Unibuc - NLP team in tackling the SemEval 2025 Workshop, Task 11: Bridging the Gap in Text-Based Emotion Detection. We mainly focused on experiments using large language models (Gemini, Qwen, DeepSeek) with either few-shot prompting or fine-tuning. Withour final system, for the multi-label emotion detection track (track A), we got an F1-macro of 0.7546 (26/96 teams) for the English subset, 0.1727 (35/36 teams) for the Portuguese (Mozambican) subset and 0.325 (1/31 teams) for the Emakhuwa subset.</abstract>
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%0 Conference Proceedings
%T Team Unibuc - NLP at SemEval-2025 Task 11: Few-shot text-based emotion detection
%A Creanga, Claudiu
%A Marchitan, Teodor -. George
%A Dinu, Liviu
%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 creanga-etal-2025-team-unibuc
%X This paper describes the approach of the Unibuc - NLP team in tackling the SemEval 2025 Workshop, Task 11: Bridging the Gap in Text-Based Emotion Detection. We mainly focused on experiments using large language models (Gemini, Qwen, DeepSeek) with either few-shot prompting or fine-tuning. Withour final system, for the multi-label emotion detection track (track A), we got an F1-macro of 0.7546 (26/96 teams) for the English subset, 0.1727 (35/36 teams) for the Portuguese (Mozambican) subset and 0.325 (1/31 teams) for the Emakhuwa subset.
%U https://aclanthology.org/2025.semeval-1.65/
%P 468-475
Markdown (Informal)
[Team Unibuc - NLP at SemEval-2025 Task 11: Few-shot text-based emotion detection](https://aclanthology.org/2025.semeval-1.65/) (Creanga et al., SemEval 2025)
ACL