Font shapes can evoke a wide range of impressions, but the correspondence between fonts and impression descriptions is not one-to-one: some impressions are broadly compatible with diverse styles, whereas others strongly constrain the set of plausible fonts. We refer to this graded constraint strength as style specificity. In this paper, we propose a hyperbolic co-embedding framework that models font--impression correspondence through entailment rather than simple paired alignment. Font images and impression descriptions, represented as single tags or tag sets, are embedded in a shared hyperbolic space with two complementary entailment constraints: impression-to-font entailment and low-to-high style-specificity entailment among impressions. This formulation induces a radial structure in which low style-specificity impressions lie near the origin and high style-specificity impressions lie farther away, yielding an interpretable geometric measure of how strongly an impression constrains font style. Experiments on the MyFonts dataset demonstrate improved bidirectional retrieval over strong one-to-one baselines. In addition, traversal and tag-level analyses show that the learned space captures a coherent progression from ambiguous to more style-specific impressions and provides a meaningful, data-driven quantification of style specificity.

翻译：字体形状可引发广泛的印象联想，但字体与印象描述之间并非一一对应：部分印象与多样化的风格广泛兼容，而另一些则对可能的字体集合构成强烈约束。我们将这种梯度化的约束强度定义为风格特异性。本文提出一种双曲协同嵌入框架，通过蕴含关系（而非简单的配对对齐）建模字体-印象对应。字体图像与以单一标签或标签集合形式呈现的印象描述，被嵌入到共享的双曲空间中，并施加两种互补的蕴含约束：印象到字体的蕴含，以及印象间从低到高风格特异性的蕴含。该公式诱导出辐射状结构，其中低风格特异性印象位于原点附近，高风格特异性印象则分布得更远，从而为印象对字体风格约束的强度提供了可解释的几何度量。在MyFonts数据集上的实验表明，相较于强基线的一对一方法，本方法在双向检索上取得了改进。此外，遍历分析与标签级分析显示，学习到的空间捕捉了从模糊到更具风格特异性印象的连贯演变，并提供了基于数据的、有意义的风格特异性量化。