It achieves this by constructing real vector embeddings using bilinear logistic regression with nonzero word co-occurrences in the training corpus within a specific context.
we show that these models attain a structural composition and semantic representation that is closer to the way humans represent concepts, evaluated using human similarity judgements, human semantic feature knowledge, and neuroimaging data.