However, a stronger test of how closely an embedding model relates to human-elicited conceptual knowledge is to investigate whether the embedding dimensions encode interpretable, human-like semantic properties directly.
does an embedding model learn a set of basis vectors for the semantic space that corresponds to verbalisable, human semantic properties like is-round, is-a-fruit, and so on?