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.
ACL