针对跨语言短文本多情绪检测，提出动态适配的特征中心框架。
PromotionGo at SemEval-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts
- 基于文档表示、降维与模型训练三环节，动态优化多语言表现。
- 低资源语言中TF-IDF效果优于深度模型，而句向量在高资源语种更优。
- PCA加速训练且不损失性能，适合快速部署于资源受限场景。
本文介绍我们在SemEval 2025任务11（基于文本的情绪检测：赛道A）中的系统，专注于短文本的多标签情绪识别。我们提出一种特征中心框架，通过动态调整文档表示与学习算法以优化各语言表现。研究评估了28种语言中的三大组件：文档表示、降维方法和模型训练，重点分析其中五种语言。结果表明，低资源语言中TF-IDF仍具高效性；而诸如FastText和基于Transformer的句向量（如Sentence-BERT）在高资源语言中展现显著优势。主成分分析（PCA）可有效降低训练时间，且对FastText及多层感知机（MLP）等神经模型性能无损。计算效率分析揭示了模型复杂度与处理成本之间的权衡。该框架为多语言情绪检测提供了可扩展解决方案，有效应对语言多样性与资源限制挑战。
原文摘要 · Abstract (English)
This paper presents our system for SemEval 2025 Task 11: Bridging the Gap in Text-Based Emotion Detection (Track A), which focuses on multi-label emotion detection in short texts. We propose a feature-centric framework that dynamically adapts document representations and learning algorithms to optimize language-specific performance. Our study evaluates three key components: document representation, dimensionality reduction, and model training in 28 languages, highlighting five for detailed analysis. The results show that TF-IDF remains highly effective for low-resource languages, while contextual embeddings like FastText and transformer-based document representations, such as those produced by Sentence-BERT, exhibit language-specific strengths. Principal Component Analysis (PCA) reduces training time without compromising performance, particularly benefiting FastText and neural models such as Multi-Layer Perceptrons (MLP). Computational efficiency analysis underscores the trade-off between model complexity and processing cost. Our framework provides a scalable solution for multilingual emotion detection, addressing the challenges of linguistic diversity and resource constraints.
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