For our purposes, however, instead of learning a global linear transformation function in two spaces over a broad relation like hypernymy, we learn a function sensitive to a given do-main of knowledge.
our training data becomes restricted to those term-hypernym BabelNet sense pairs For each domain-wise expanded training set T d , we construct a hyponym matrix , which are composed by the corresponding SENSEMBED vectors of the training pairs Under the intuition that there exists a matrix Ψ so that y d = Ψ x d , we learn a transformation matrix for each domain cluster C d by minimizing: Then, for any unseen term x d , we obtain a ranked list of the most likely hypernyms of its lexicalization vectors x j d , using as measure cosine similarity: At this point, we have associated with each sense vector a ranked list of candidate hypernym vectors.