  Vector space word representations are learned from distributional information
of words in large corpora. Although such statistics are semantically
informative, they disregard the valuable information that is contained in
semantic lexicons such as WordNet, FrameNet, and the Paraphrase Database. This
paper proposes a method for refining vector space representations using
relational information from semantic lexicons by encouraging linked words to
have similar vector representations, and it makes no assumptions about how the
input vectors were constructed. Evaluated on a battery of standard lexical
semantic evaluation tasks in several languages, we obtain substantial
improvements starting with a variety of word vector models. Our refinement
method outperforms prior techniques for incorporating semantic lexicons into
the word vector training algorithms.
