Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions

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 …

constraints cs.cl cs.lg definitions embeddings language natural natural language paper recursive relational relations representation representation learning semantic space support word word embeddings