Since the Mult model works very poorly when the input vectors contain negative values, as is the case with SVD, for this model we report result distributions across the 6 NMF variations only.
we used the word cooccurrence vector for a verb or an adjective extracted from the corpus and projected onto the reduced feature space (e.g., NMF, 100 dimensions), then computed cosine similarity between this word meaning representation and its corresponding EnetLex matrix initial and minimumerror intercepts, respectively.