基于Busemann能量的注意力机制用于Poincaré盘中的情感分析
Busemann energy-based attention for emotion analysis in Poincaré discs
AI总结：
本文提出EmBolic，一种全新的全双曲深度学习架构，用于从文本消息中进行细粒度情感分析。通过双曲几何捕捉词与情感间的层次结构，利用双曲盘中的注意力机制生成查询并计算Busemann能量以预测情感。
摘要：
我们提出了EmBolic——一种新颖的全双曲深度学习架构，用于从文本消息中进行细粒度情感分析。其核心思想是双曲几何能有效捕捉词与情感之间的层次结构。在我们的上下文中，这些层次关系源于语义歧义。EmBolic旨在推断情感连续空间的曲率，而非将情感视为无度量结构的分类集。架构核心是双曲盘中的注意力机制。模型训练生成查询（双曲盘中的点）从文本消息，而键（边界上的点）自动从生成的查询中出现。预测基于查询与键之间的Busemann能量，评估文本消息与代表情感的类别方向的匹配程度。实验表明模型具有良好的泛化能力和合理预测精度，即使在表示空间小维度时也是如此。总体而言，本研究支持我们的观点，即情感计算是双曲表示特别有优势的应用领域之一。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
