  Hypernymy plays a fundamental role in many AI tasks like taxonomy learning,
ontology learning, etc. This has motivated the development of many automatic
identification methods for extracting this relation, most of which rely on word
distribution. We present a novel model HyperBox to learn box embeddings for
hypernym discovery. Given an input term, HyperBox retrieves its suitable
hypernym from a target corpus. For this task, we use the dataset published for
SemEval 2018 Shared Task on Hypernym Discovery. We compare the performance of
our model on two specific domains of knowledge: medical and music.
Experimentally, we show that our model outperforms existing methods on the
majority of the evaluation metrics. Moreover, our model generalize well over
unseen hypernymy pairs using only a small set of training data.
