We propose to calculate semantic similarity using distributed neural embedding models, instead of deriving it from a co-occurrence matrix directly (the latter is a socalled count-based method).
we test distributed representations of words produced by the Continuous SkipGram algorithm, first implemented in the word2vec tool (Mikolov et al., 2013).