Abstract
Discriminating antonyms and synonyms is an
important NLP task that has the difficulty that
both, antonyms and synonyms, contains similar distributional information. Consequently,
pairs of antonyms and synonyms may have
similar word vectors. We present an approach to unravel antonymy and synonymy
from word vectors based on a siamese network
inspired approach. The model consists of a
two-phase training of the same base network:
a pre-training phase according to a siamese
model supervised by synonyms and a training phase on antonyms through a siamese-like
model that supports the antitransitivity present
in antonymy. The approach makes use of the
claim that the antonyms in common of a word
tend to be synonyms. We show that our approach outperforms distributional and patternbased approaches, relaying on a simple feed
forward network as base network of the training phases.