In this paper, we present Non-Orthogonal ESA (NESA) which represents more fine grained semantics of a word as a vector of explicit concept dimensions, where every such concept dimension further constitutes a semantic vector built in another vector space.
nESA considers the concept correlations in computing the relatedness between two words.