  In Distributional Semantic Models (DSMs), Vector Cosine is widely used to
estimate similarity between word vectors, although this measure was noticed to
suffer from several shortcomings. The recent literature has proposed other
methods which attempt to mitigate such biases. In this paper, we intend to
investigate APSyn, a measure that computes the extent of the intersection
between the most associated contexts of two target words, weighting it by
context relevance. We evaluated this metric in a similarity estimation task on
several popular test sets, and our results show that APSyn is in fact highly
competitive, even with respect to the results reported in the literature for
word embeddings. On top of it, APSyn addresses some of the weaknesses of Vector
Cosine, performing well also on genuine similarity estimation.
