Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence   and Vector Cosine in VSMs
  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. 
Enrico Santus, Tin-Shing Chiu, Qin Lu, Alessandro Lenci and Chu-Ren   Huang
cs.CL