  We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing
method that transforms any input word vector space to emphasise the asymmetric
relation of lexical entailment (LE), also known as the IS-A or
hyponymy-hypernymy relation. By injecting external linguistic constraints
(e.g., WordNet links) into the initial vector space, the LE specialisation
procedure brings true hyponymy-hypernymy pairs closer together in the
transformed Euclidean space. The proposed asymmetric distance measure adjusts
the norms of word vectors to reflect the actual WordNet-style hierarchy of
concepts. Simultaneously, a joint objective enforces semantic similarity using
the symmetric cosine distance, yielding a vector space specialised for both
lexical relations at once. LEAR specialisation achieves state-of-the-art
performance in the tasks of hypernymy directionality, hypernymy detection, and
graded lexical entailment, demonstrating the effectiveness and robustness of
the proposed asymmetric specialisation model.
