CamemBERT

Comparative study of semantic geometry in transformer embeddings vs. graph-based lexical models

A preprint from arXiv compares the geometric and topological properties of transformer-based vector embeddings (CamemBERT) against lexical co-occurrence graphs for representing semantic structure. Applied to a French civic debate corpus, the study finds similar local topology but divergent global structure between the two approaches. The authors argue graph-based models offer more interpretable semantic organization and suggest graphs could guide neural architectures toward more stable, interpretable convergence.