Information about a specific semantic property can be stored latently over the dimensions of a semantic embedding model, such that the semantic prop-erty can be reliably decoded given an embedding vector, as tested in the previous section.
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.