  This work presents an unsupervised approach for improving WordNet that builds
upon recent advances in document and sense representation via distributional
semantics. We apply our methods to construct Wordnets in French and Russian,
languages which both lack good manual constructions.1 These are evaluated on
two new 600-word test sets for word-to-synset matching and found to improve
greatly upon synset recall, outperforming the best automated Wordnets in
F-score. Our methods require very few linguistic resources, thus being
applicable for Wordnet construction in low-resources languages, and may further
be applied to sense clustering and other Wordnet improvements.
