  One major deficiency of most semantic representation techniques is that they
usually model a word type as a single point in the semantic space, hence
conflating all the meanings that the word can have. Addressing this issue by
learning distinct representations for individual meanings of words has been the
subject of several research studies in the past few years. However, the
generated sense representations are either not linked to any sense inventory or
are unreliable for infrequent word senses. We propose a technique that tackles
these problems by de-conflating the representations of words based on the deep
knowledge it derives from a semantic network. Our approach provides multiple
advantages in comparison to the past work, including its high coverage and the
ability to generate accurate representations even for infrequent word senses.
We carry out evaluations on six datasets across two semantic similarity tasks
and report state-of-the-art results on most of them.
