Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions
This work addresses the challenge of capturing semantic structure in word embeddings for natural language processing, offering an incremental improvement through a multi-relational approach.
The paper tackled the problem of learning word embeddings from natural language definitions by formalizing it as a translational framework in Hyperbolic space, resulting in embeddings that outperform Euclidean counterparts and achieve competitive results with state-of-the-art neural models while being more efficient and interpretable.
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.