The paper investigates compositional semantic models specialised for metaphors.

https://i.imgur.com/OnoJK3h.png

They construct a dataset of 8592 adjective-noun phrases, covering 23 different adjectives, annotated for being metaphorical or literal. They then train compositional models to predict the phrase vector based on the noun vector, as a linear combination with an adjective-specific weight matrix. They show that it’s better to learn separate adjective matrices for literal and metaphorical uses of each adjective, even though the amount of training data is smaller.