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Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions

文献类型： 会议论文

第一作者： Marco Valentino

作者： Marco Valentino 1 ; Danilo S. Carvalho 2 ; Andre Freitas 3 ;

作者机构： 1.Idiap Research Institute, Switzerland

2.Department of Computer Science, University of Manchester, UK##National Biomarker Centre, CRUK-MI, University of Manchester, UK

3.Idiap Research Institute, Switzerland##Department of Computer Science, University of Manchester, UK##National Biomarker Centre, CRUK-MI, University of Manchester, UK

会议名称： Conference of the European Chapter of the Association for Computational Linguistics

主办单位：

页码： 23-34

摘要： 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 em-beddings 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.

分类号： tp311

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