Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction

Understand

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.