Toward a Functional Geometric Algebra for Natural Language Semantics

ArXi:2604.25902v1 Announce Type: cross Distributional and neural approaches to natural language semantics have been built almost exclusively on conventional linear algebra: vectors, matrices, tensors, and the operations that accompany them. These methods have achieved remarkable empirical success, yet they face persistent structural limitations in compositional semantics, type sensitivity, and interpretability.