Distributional models provide a convenient way to model semantics using dense embedding spaces derived from unsupervised learning algorithms.
as a further test of how well dimensions of embedding models correspond to human semantic knowledge, we calculated pairwise correlations, across concepts, between embedding dimensions and properties.