title: Mapping the hyponymy relation of wordnet onto vector Spaces
abstract:  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.
  
authors: Jean-Philippe Bernardy Aleksandre Maskharashvili
hIndex: Jean-Philippe Bernardy 14