2025
SEMEVAL
SemEval 2025
NLP_goats at SemEval-2025 Task 11: Multi-Label Emotion Classification Using Fine-Tuned Roberta-Large Tranformer
Abstract
AbstractThis paper serves as a solution for multi-label emotion classification and intensity for text, developed for SemEval-2025 Task 11. The method uses a fine-tuned RoBERTa-Large transformer model. The system represents a multi-label classification approach to identifying multiple emotions, and uses regression models to estimate emotion strength. The model performed with ranks of 31st and 17th place in the corresponding tracks. The findings show impressive performance and it remains possible to improve the performance of ambiguous or low-frequency emotion recognition using the state-of-the-art contextual embeddings and threshold optimization techniques.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Regression
Natural Language Processing > Applications > Sentiment Analysis
Machine Learning > Learning Types > Fine-Tuning
Deep Learning > Models > Transformers