@inproceedings{vaidyanathan-etal-2025-nlp-goats,
title = "{NLP}{\_}goats at {S}em{E}val-2025 Task 11: Multi-Label Emotion Classification Using Fine-Tuned Roberta-Large Tranformer",
author = "Vaidyanathan, Vijay Karthick and
V K, Srihari and
D U, Mugilkrishna and
Madhavan, Saritha",
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-1.135/",
pages = "1023--1027",
ISBN = "979-8-89176-273-2",
abstract = "This 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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<abstract>This 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.</abstract>
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%0 Conference Proceedings
%T NLP_goats at SemEval-2025 Task 11: Multi-Label Emotion Classification Using Fine-Tuned Roberta-Large Tranformer
%A Vaidyanathan, Vijay Karthick
%A V K, Srihari
%A D U, Mugilkrishna
%A Madhavan, Saritha
%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 vaidyanathan-etal-2025-nlp-goats
%X This 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.
%U https://aclanthology.org/2025.semeval-1.135/
%P 1023-1027
Markdown (Informal)
[NLP_goats at SemEval-2025 Task 11: Multi-Label Emotion Classification Using Fine-Tuned Roberta-Large Tranformer](https://aclanthology.org/2025.semeval-1.135/) (Vaidyanathan et al., SemEval 2025)
ACL