Geometry of Semantic Space: Comparative Study of Discrete and Continuous Models 事件

Geometry of Semantic Space: Comparative Study of Discrete and Continuous Models arXiv:2606.07183v1 Announce Type: new Abstract: 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 rev