  In this paper, we outline an approach to build graph-based reverse
dictionaries using word definitions. A reverse dictionary takes a phrase as an
input and outputs a list of words semantically similar to that phrase. It is a
solution to the Tip-of-the-Tongue problem. We use a distance-based similarity
measure, computed on a graph, to assess the similarity between a word and the
input phrase. We compare the performance of our approach with the Onelook
Reverse Dictionary and a distributional semantics method based on word2vec, and
show that our approach is much better than the distributional semantics method,
and as good as Onelook, on a 3k lexicon. This simple approach sets a new
performance baseline for reverse dictionaries.
