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.
Also, REF integrated synonymy and antonymy information into the skip-gram model to predict word similarity and distinguish synonyms and antonyms.
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction
ArXiv
ArXiv
Computer Science