  Discriminating lexical relations among distributionally similar words has
always been a challenge for natural language processing (NLP) community. In
this paper, we investigate whether the network embedding of distributional
thesaurus can be effectively utilized to detect co-hyponymy relations. By
extensive experiments over three benchmark datasets, we show that the vector
representation obtained by applying node2vec on distributional thesaurus
outperforms the state-of-the-art models for binary classification of
co-hyponymy vs. hypernymy, as well as co-hyponymy vs. meronymy, by huge
margins.
