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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 bydoi:10.18653/v1/p19-1319 dblp:conf/acl/EtcheverryW19 fatcat:gogkuex7qfbyvdj6ya4asthmfe