  We present a novel neural model HyperVec to learn hierarchical embeddings for
hypernymy detection and directionality. While previous embeddings have shown
limitations on prototypical hypernyms, HyperVec represents an unsupervised
measure where embeddings are learned in a specific order and capture the
hypernym$-$hyponym distributional hierarchy. Moreover, our model is able to
generalize over unseen hypernymy pairs, when using only small sets of training
data, and by mapping to other languages. Results on benchmark datasets show
that HyperVec outperforms both state$-$of$-$the$-$art unsupervised measures and
embedding models on hypernymy detection and directionality, and on predicting
graded lexical entailment.
