  Natural language definitions possess a recursive, self-explanatory semantic
structure that can support representation learning methods able to preserve
explicit conceptual relations and constraints in the latent space. This paper
presents a multi-relational model that explicitly leverages such a structure to
derive word embeddings from definitions. By automatically extracting the
relations linking defined and defining terms from dictionaries, we demonstrate
how the problem of learning word embeddings can be formalised via a
translational framework in Hyperbolic space and used as a proxy to capture the
global semantic structure of definitions. An extensive empirical analysis
demonstrates that the framework can help imposing the desired structural
constraints while preserving the semantic mapping required for controllable and
interpretable traversal. Moreover, the experiments reveal the superiority of
the Hyperbolic word embeddings over the Euclidean counterparts and demonstrate
that the multi-relational approach can obtain competitive results when compared
to state-of-the-art neural models, with the advantage of being intrinsically
more efficient and interpretable.
