In addition to represent the words as distributional vectors, where each dimension is associated with a Wikipedia concept as in ESA model, NESA also utilizes a square matrix C n,n (n is the total number of dimensions) containing the correlation weights between the dimensions.
to obtain the relatedness score between the words w1 and w2, NESA formulates the measure as follows: (1) where w1 n,1 and w2 n,1 are the corresponding distributional vectors consisting of n dimensions.