  Word embeddings have been shown to be useful across state-of-the-art systems
in many natural language processing tasks, ranging from question answering
systems to dependency parsing. (Herbelot and Vecchi, 2015) explored word
embeddings and their utility for modeling language semantics. In particular,
they presented an approach to automatically map a standard distributional
semantic space onto a set-theoretic model using partial least squares
regression. We show in this paper that a simple baseline achieves a +51%
relative improvement compared to their model on one of the two datasets they
used, and yields competitive results on the second dataset.
