  Advances in word representations have shown tremendous improvements in
downstream NLP tasks, but lack semantic interpretability. In this paper, we
introduce Definition Frames (DF), a matrix distributed representation extracted
from definitions, where each dimension is semantically interpretable. DF
dimensions correspond to the Qualia structure relations: a set of relations
that uniquely define a term. Our results show that DFs have competitive
performance with other distributional semantic approaches on word similarity
tasks.
