They propose using attribute-based vectors for detecting metaphorical word pairs.

https://i.imgur.com/dgDjSvu.png

Traditional embeddings (word2vec and count-based) are mapped to attribute vectors, using a supervised system trained on McRae norms. These vectors for a word pair are then given as input to an SVM classifier and trained to detect metaphorical (black humour) vs literal (black dress) word pairs. They show that using the attribute vectors gives higher F score over using the original vector space.