Mapping the hyponymy relation of wordnet onto vector Spaces

Jean-Philippe Bernardy, Aleksandre Maskharashvili

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.

Keywords: fasttext, hyponymy, wordnet

TL;DR: We investigate mapping the hyponymy relation of wordnet to feature vectors