用几何方法分析汉字嵌入，发现意义字复杂、形符字集中。
Geometric Structures and Patterns of Meaning: A PHATE Manifold Analysis of Chinese Character Embeddings
- 通过PHATE降维揭示汉字嵌入的几何结构
- 意义字呈现复杂分布，形符字聚成紧密簇
- 适合对中文语义结构感兴趣的读者
我们系统研究了使用PHATE流形分析的汉字嵌入中的几何模式。通过对七种嵌入模型和八种降维方法进行交叉验证，观察到实词呈现聚类模式，虚词呈现分支模式。在12个语义领域中对超过1000个汉字的分析显示，几何复杂性与语义内容相关：有意义的字符表现出丰富的几何多样性，而结构部首则聚集为紧密簇。对123个词语的综合性子网络分析表明，语义从基本字符开始系统扩展。这些发现为传统语言学理论提供了计算支持，并建立了一种分析语义组织的新几何框架。
原文摘要 · Abstract (English)
We systematically investigate geometric patterns in Chinese character embeddings using PHATE manifold analysis. Through cross-validation across seven embedding models and eight dimensionality reduction methods, we observe clustering patterns for content words and branching patterns for function words. Analysis of over 1000 Chinese characters across 12 semantic domains reveals that geometric complexity correlates with semantic content: meaningful characters exhibit rich geometric diversity while structural radicals collapse into tight clusters. The comprehensive child-network analysis (123 phrases) demonstrates systematic semantic expansion from elemental character. These findings provide computational evidence supporting traditional linguistic theory and establish a novel framework for geometric analysis of semantic organization.
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