  Word embeddings are widely used in Natural Language Processing, mainly due to
their success in capturing semantic information from massive corpora. However,
their creation process does not allow the different meanings of a word to be
automatically separated, as it conflates them into a single vector. We address
this issue by proposing a new model which learns word and sense embeddings
jointly. Our model exploits large corpora and knowledge from semantic networks
in order to produce a unified vector space of word and sense embeddings. We
evaluate the main features of our approach both qualitatively and
quantitatively in a variety of tasks, highlighting the advantages of the
proposed method in comparison to state-of-the-art word- and sense-based models.
