用双曲几何建模情绪层次，提升文本情感分析精度
Busemann energy-based attention for emotion analysis in Poincaré discs
- 在双曲盘中设计基于布塞曼能量的注意力机制
- 小维度下仍保持良好泛化与预测准确率
- 适合处理具有层级结构的情绪分析任务
我们提出EmBolic——一种全新的全双曲深度学习架构，用于细粒度文本情感分析。核心思想是利用双曲几何高效捕捉词语与情绪之间的层次关系，这些关系源于语义模糊性。与将情绪视为无度量结构的类别集合不同，EmBolic旨在推断情绪连续空间中的曲率。模型通过文本消息生成查询点（位于双曲盘内部），键点（位于边界）则由查询点自动衍生。预测基于查询点与键点之间的布塞曼能量，评估文本与情绪方向的匹配程度。实验表明，该模型即使在低维表示空间下也展现出强泛化能力与合理准确率。本研究支持了情感计算是双曲表示特别有益的应用领域这一观点。
原文摘要 · Abstract (English)
We present EmBolic - a novel fully hyperbolic deep learning architecture for fine-grained emotion analysis from textual messages. The underlying idea is that hyperbolic geometry efficiently captures hierarchies between both words and emotions. In our context, these hierarchical relationships arise from semantic ambiguities. EmBolic aims to infer the curvature on the continuous space of emotions, rather than treating them as a categorical set without any metric structure. In the heart of our architecture is the attention mechanism in the hyperbolic disc. The model is trained to generate queries (points in the hyperbolic disc) from textual messages, while keys (points at the boundary) emerge automatically from the generated queries. Predictions are based on the Busemann energy between queries and keys, evaluating how well a certain textual message aligns with the class directions representing emotions. Our experiments demonstrate strong generalization properties and reasonably good prediction accuracy even for small dimensions of the representation space. Overall, this study supports our claim that affective computing is one of the application domains where hyperbolic representations are particularly advantageous.
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