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        <title>JellyK at SemEval-2025 Task 11: Russian Multi-label Emotion Detection with Pre-trained BERT-based Language Models</title>
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    <abstract>This paper presents our approach for SemEval-2025 Task 11, we focus on on multi-label emotion detection in Russian text (track A). We preprocess the data by handling special characters, punctuation, and emotive expressions to improve feature-label relationships. To select the best model performance, we fine-tune various pre-trained language models specialized in Russian and evaluate them using K-FOLD Cross-Validation. Our results indicated that ruRoberta-large achieved the best Macro F1-score among tested models. Finally, our system achieved fifth place in the unofficial competition ranking.</abstract>
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%0 Conference Proceedings
%T JellyK at SemEval-2025 Task 11: Russian Multi-label Emotion Detection with Pre-trained BERT-based Language Models
%A Le, Khoa
%A Thin, Dang
%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 le-thin-2025-jellyk
%X This paper presents our approach for SemEval-2025 Task 11, we focus on on multi-label emotion detection in Russian text (track A). We preprocess the data by handling special characters, punctuation, and emotive expressions to improve feature-label relationships. To select the best model performance, we fine-tune various pre-trained language models specialized in Russian and evaluate them using K-FOLD Cross-Validation. Our results indicated that ruRoberta-large achieved the best Macro F1-score among tested models. Finally, our system achieved fifth place in the unofficial competition ranking.
%U https://aclanthology.org/2025.semeval-1.272/
%P 2090-2095

Markdown (Informal)

[JellyK at SemEval-2025 Task 11: Russian Multi-label Emotion Detection with Pre-trained BERT-based Language Models](https://aclanthology.org/2025.semeval-1.272/) (Le & Thin, SemEval 2025)

• JellyK at SemEval-2025 Task 11: Russian Multi-label Emotion Detection with Pre-trained BERT-based Language Models (Le & Thin, SemEval 2025)

ACL

• Khoa Le and Dang Thin. 2025. JellyK at SemEval-2025 Task 11: Russian Multi-label Emotion Detection with Pre-trained BERT-based Language Models. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2090–2095, Vienna, Austria. Association for Computational Linguistics.