This paper proposes a novel approach to train word embeddings to capture antonyms. Word embeddings have shown to capture synonyms and analogies. Such word embeddings, however, cannot capture antonyms since they depend on the distributional hypothesis. Our approach utilizes supervised synonym and antonym information from thesauri, as well as distributional information from large-scale unlabelled text data. The evaluation results on the GRE antonym question task show that our model outperforms the state-of-the-art systems and it can answer the antonym questions in the F-score of 89%.
REF proposed a word embedding-based approach using supervised synonym and antonym information from thesauri, and distributional information from large-scale unlabeled text data and reported improved results.
Word Embedding-based Antonym Detection using Thesauri and Distributional Information
HLT-NAACL
Computer Science