In this paper, we combine both text and image-based data in conjunction with matrix factorisation strategies to build sparse and multimodal distributional models, with the goal of demonstrating that these models are more interpretable with respect to human semantic knowledge about concepts.
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.