  In this paper, we claim that vector cosine, which is generally considered
among the most efficient unsupervised measures for identifying word similarity
in Vector Space Models, can be outperformed by an unsupervised measure that
calculates the extent of the intersection among the most mutually dependent
contexts of the target words. To prove it, we describe and evaluate APSyn, a
variant of the Average Precision that, without any optimization, outperforms
the vector cosine and the co-occurrence on the standard ESL test set, with an
improvement ranging between +9.00% and +17.98%, depending on the number of
chosen top contexts.
