Integrating Distributional Lexical Contrast into Word Embeddings for   Antonym-Synonym Distinction
  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 from synonyms. 
Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
cs.CL