@inproceedings{waheed-etal-2025-habib,
title = "Habib University at {S}em{E}val-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection",
author = "Waheed, Owais and
Sajid, Hammad and
Chandani, Kushal and
Kazmi, Muhammad Areeb and
Kumar, Sandesh and
Samad, Abdul",
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-1.217/",
pages = "1656--1661",
ISBN = "979-8-89176-273-2",
abstract = "Emotion detection in text has emerged as a pivotal challenge in Natural Language Processing (NLP), particularly in multilingual and cross-lingual contexts. This paper presents our participation in SemEval 2025 Task 11, focusing on three subtasks: Multi-label Emotion Detection, Emotion Intensity Prediction, and Cross-lingual Emotion Detection. Leveraging state-of-the-art transformer models such as BERT and XLM-RoBERTa, we implemented baseline models and ensemble techniques to enhance predictive accuracy. Additionally, innovative approaches like data augmentation and translation-based cross-lingual emotion detection were used to address linguistic and class imbalances. Our results demonstrated significant improvements in F1 scores and Pearson correlations, showcasing the effectiveness of ensemble learning and transformer-based architectures in emotion recognition. This work advances the field by providing robust methods for emotion detection, particularly in low-resource and multilingual settings."
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<abstract>Emotion detection in text has emerged as a pivotal challenge in Natural Language Processing (NLP), particularly in multilingual and cross-lingual contexts. This paper presents our participation in SemEval 2025 Task 11, focusing on three subtasks: Multi-label Emotion Detection, Emotion Intensity Prediction, and Cross-lingual Emotion Detection. Leveraging state-of-the-art transformer models such as BERT and XLM-RoBERTa, we implemented baseline models and ensemble techniques to enhance predictive accuracy. Additionally, innovative approaches like data augmentation and translation-based cross-lingual emotion detection were used to address linguistic and class imbalances. Our results demonstrated significant improvements in F1 scores and Pearson correlations, showcasing the effectiveness of ensemble learning and transformer-based architectures in emotion recognition. This work advances the field by providing robust methods for emotion detection, particularly in low-resource and multilingual settings.</abstract>
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%0 Conference Proceedings
%T Habib University at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
%A Waheed, Owais
%A Sajid, Hammad
%A Chandani, Kushal
%A Kazmi, Muhammad Areeb
%A Kumar, Sandesh
%A Samad, Abdul
%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 waheed-etal-2025-habib
%X Emotion detection in text has emerged as a pivotal challenge in Natural Language Processing (NLP), particularly in multilingual and cross-lingual contexts. This paper presents our participation in SemEval 2025 Task 11, focusing on three subtasks: Multi-label Emotion Detection, Emotion Intensity Prediction, and Cross-lingual Emotion Detection. Leveraging state-of-the-art transformer models such as BERT and XLM-RoBERTa, we implemented baseline models and ensemble techniques to enhance predictive accuracy. Additionally, innovative approaches like data augmentation and translation-based cross-lingual emotion detection were used to address linguistic and class imbalances. Our results demonstrated significant improvements in F1 scores and Pearson correlations, showcasing the effectiveness of ensemble learning and transformer-based architectures in emotion recognition. This work advances the field by providing robust methods for emotion detection, particularly in low-resource and multilingual settings.
%U https://aclanthology.org/2025.semeval-1.217/
%P 1656-1661
Markdown (Informal)
[Habib University at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection](https://aclanthology.org/2025.semeval-1.217/) (Waheed et al., SemEval 2025)
ACL