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

Testing APSyn against Vector Cosine on Similarity Estimation