Let the training set of n instances the NCs annotated with semantic relations r 1 ....r n respectively, where r i ∈ relation set R. The input x k is represented in terms of modifier and head fea- represent synsets at level i and j in the hypernym hierarchy, combinedly represented as f ij .
the goal is to model the prediction function F : (L×L) R. The SemScat models strives to learn generalized sets of Hypernym synsets, known as Boundary, G. For instance -G 1 = {entity} and G 2 = {physical − entity, abstract − entity, thing} are two boundaries where G 2 is hyponym of G 1 .