 In this paper, we investigate mapping the hyponymy relation of
 wordnet to feature vectors.
   We aim to model lexical knowledge in such a way that it can be used as
  input in generic machine-learning models, such as phrase entailment
  predictors.
   We propose two models. The first one leverages an existing mapping of
  words to feature vectors (fasttext), and attempts to classify
  such vectors as within or outside of each class. The second model is fully supervised,
  using solely wordnet as a ground truth. It maps each concept to an
  interval or a disjunction thereof.
   On the first model, we approach, but not quite attain state of the
  art performance. The second model can achieve near-perfect accuracy.

We investigate mapping the hyponymy relation of wordnet to feature vectors This paper studies how hyponymy between words can be mapped to feature representations. This paper explores the notion of hyponymy in word vector representations and describes a method of organizing WordNet relations into a tree structure to define hyponymy.