@inproceedings{riad-ullah-2025-syntaxmind,
title = "{S}yntax{M}ind at {S}em{E}val-2025 Task 11: {BERT} Base Multi-label Emotion Detection Using Gated Recurrent Unit",
author = "Riad, Md. Shihab Uddin and
Ullah, Mohammad Aman",
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-1.191/",
pages = "1450--1455",
ISBN = "979-8-89176-273-2",
abstract = "Emotions influence human behavior, speech, and expression, making their detection crucial in Natural Language Processing (NLP). While most prior research has focused on single-label emotion classification, real-world emotions are often multi-faceted. This paper describes our participation in SemEval-2025 Task 11, Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity). We employed BERT as a feature extractor with stacked GRUs, which resulted in better stability and convergence. Our system was evaluated across 19 languages for Track A and 9 languages for Track B."
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<abstract>Emotions influence human behavior, speech, and expression, making their detection crucial in Natural Language Processing (NLP). While most prior research has focused on single-label emotion classification, real-world emotions are often multi-faceted. This paper describes our participation in SemEval-2025 Task 11, Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity). We employed BERT as a feature extractor with stacked GRUs, which resulted in better stability and convergence. Our system was evaluated across 19 languages for Track A and 9 languages for Track B.</abstract>
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%0 Conference Proceedings
%T SyntaxMind at SemEval-2025 Task 11: BERT Base Multi-label Emotion Detection Using Gated Recurrent Unit
%A Riad, Md. Shihab Uddin
%A Ullah, Mohammad Aman
%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 riad-ullah-2025-syntaxmind
%X Emotions influence human behavior, speech, and expression, making their detection crucial in Natural Language Processing (NLP). While most prior research has focused on single-label emotion classification, real-world emotions are often multi-faceted. This paper describes our participation in SemEval-2025 Task 11, Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity). We employed BERT as a feature extractor with stacked GRUs, which resulted in better stability and convergence. Our system was evaluated across 19 languages for Track A and 9 languages for Track B.
%U https://aclanthology.org/2025.semeval-1.191/
%P 1450-1455
Markdown (Informal)
[SyntaxMind at SemEval-2025 Task 11: BERT Base Multi-label Emotion Detection Using Gated Recurrent Unit](https://aclanthology.org/2025.semeval-1.191/) (Riad & Ullah, SemEval 2025)
ACL