Empaths at SemEval-2025 Task 11: Retrieval-Augmented Approach to Perceived Emotions Prediction

Fuente: arXiv
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Auteurs principaux: Morozov, Lev, Mogilevskii, Aleksandr, Shirnin, Alexander
Format: Preprint
Publié: 2025
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author Morozov, Lev
Mogilevskii, Aleksandr
Shirnin, Alexander
author_facet Morozov, Lev
Mogilevskii, Aleksandr
Shirnin, Alexander
contents This paper describes EmoRAG, a system designed to detect perceived emotions in text for SemEval-2025 Task 11, Subtask A: Multi-label Emotion Detection. We focus on predicting the perceived emotions of the speaker from a given text snippet, labeling it with emotions such as joy, sadness, fear, anger, surprise, and disgust. Our approach does not require additional model training and only uses an ensemble of models to predict emotions. EmoRAG achieves results comparable to the best performing systems, while being more efficient, scalable, and easier to implement.
format Preprint
id arxiv_https___arxiv_org_abs_2506_04409
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Empaths at SemEval-2025 Task 11: Retrieval-Augmented Approach to Perceived Emotions Prediction
Morozov, Lev
Mogilevskii, Aleksandr
Shirnin, Alexander
Computation and Language
Artificial Intelligence
This paper describes EmoRAG, a system designed to detect perceived emotions in text for SemEval-2025 Task 11, Subtask A: Multi-label Emotion Detection. We focus on predicting the perceived emotions of the speaker from a given text snippet, labeling it with emotions such as joy, sadness, fear, anger, surprise, and disgust. Our approach does not require additional model training and only uses an ensemble of models to predict emotions. EmoRAG achieves results comparable to the best performing systems, while being more efficient, scalable, and easier to implement.
title Empaths at SemEval-2025 Task 11: Retrieval-Augmented Approach to Perceived Emotions Prediction
topic Computation and Language
Artificial Intelligence
url https://arxiv.org/abs/2506.04409