无需训练，用集成模型高效预测文本中的多重情绪。
Empaths at SemEval-2025 Task 11: Retrieval-Augmented Approach to Perceived Emotions Prediction
- 采用无需训练的集成模型方法，直接预测文本情绪。
- 在多标签情绪检测任务中表现接近顶尖系统。
- 适合追求高效、易部署情绪分析方案的开发者。
本文介绍EmoRAG系统，用于SemEval-2025 Task 11 Subtask A：多标签情绪检测。该任务旨在从给定文本片段中预测说话者所传达的情绪，包括快乐、悲伤、恐惧、愤怒、惊讶和厌恶等。本方法不需额外模型训练，仅通过多个模型的集成实现情绪预测。EmoRAG在性能上可媲美最佳系统，同时具备更高的效率、可扩展性和实现便捷性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。