对比了神经网络与图模型的语义空间结构，发现后者更清晰可读。
Geometry of Semantic Space: Comparative Study of Discrete and Continuous Models
- 用图结构建模语义关系，比传统向量嵌入更直观。
- 在法国全民辩论语料上，两者局部结构相似但整体拓扑差异大。
- 适合关注模型可解释性与语义结构的研究者。
本文研究自然语言模型背后的语义几何结构。比较了监督式向量嵌入（如CamemBERT）与基于词汇共现的图模型，后者更直接编码语义关系。尽管基于Transformer的嵌入表现优异，其诱导出的几何分布常不理想；而图模型则展现出更清晰、更符合人类认知的意义组织。我们提出一种方法，可基于图结构或嵌入拓扑进行对比分析。实验基于法语“全民大辩论”语料库（包含公民公共讨论文本），结果显示两者具有相似的局部拓扑，但整体结构和拓扑显著不同。这些发现表明，深度监督模型与图模型视角互补，为引导神经架构向更稳定、可解释的收敛方向发展提供了新路径。
原文摘要 · Abstract (English)
This work examines the semantic geometry underlying NLP models. We compare supervised vector embeddings, such as CamemBERT, with lexical co-occurrence graphs that encode semantic relations more directly. While transformer-based embeddings achieve strong performance, their induced geometries often display unsatisfactory distributions. In contrast, graph-based models reveal a clearer and more human-readable organization of meaning. We have implemented a methodology that allows us to perform a comparative analysis either based on the structure of the graphs or based on the topology of the embeddings induced by these two approaches. The results of the comparison -- applied to the French "Great National Debate" corpus a collection of citizen contributions to the public debate -- show a similar local topology but a very different overall structure and topology. Theses findings suggest complementary perspectives between deep supervised models and graph-based models, considering a new pathway to guide neural architectures toward more stable and interpretable convergence with graphs structures.
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