  Neural language models encode rich knowledge about entities and their
relationships which can be extracted from their representations using probing.
Common properties of nouns (e.g., red strawberries, small ant) are, however,
more challenging to extract compared to other types of knowledge because they
are rarely explicitly stated in texts. We hypothesize this to mainly be the
case for perceptual properties which are obvious to the participants in the
communication. We propose to extract these properties from images and use them
in an ensemble model, in order to complement the information that is extracted
from language models. We consider perceptual properties to be more concrete
than abstract properties (e.g., interesting, flawless). We propose to use the
adjectives' concreteness score as a lever to calibrate the contribution of each
source (text vs. images). We evaluate our ensemble model in a ranking task
where the actual properties of a noun need to be ranked higher than other
non-relevant properties. Our results show that the proposed combination of text
and images greatly improves noun property prediction compared to powerful
text-based language models.
