Post-specialization<cite> Ponti et al., 2018</cite>; Kamath et al., 2019) is a generalization of retrofitting that specializes the entire distributional space: 1) it learns a global specialization function using before-and after-retrofitting vectors of words from lexical constraints as training examples and 2) it applies the global specialization functions to vectors of words unseen in lexical constraints.