  We investigate the hypothesis that word representations ought to incorporate
both distributional and relational semantics. To this end, we employ the
Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a
distributional objective on raw text and a relational objective on WordNet.
Preliminary results on knowledge base completion, analogy tests, and parsing
show that word representations trained on both objectives can give improvements
in some cases.
