We propose a novel vector representation that integrates lexical contrast
into distributional vectors and strengthens the most salient features for
determining degrees of word similarity. The improved vectors significantly
outperform standard models and distinguish antonyms from synonyms with an
average precision of 0.66-0.76 across word classes (adjectives, nouns, verbs)...
Moreover, we integrate the lexical contrast vectors into the objective function
of a skip-gram model. The novel embedding outperforms state-of-the-art models
on predicting word similarities in SimLex-999, and on distinguishing antonyms