Word Representations via Gaussian Embedding

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Authors Andrew McCallum, Luke Vilnis
Journal/Conference Name 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings
Paper Category
Paper Abstract Current work in lexical distributed representations maps each word to a point vector in low-dimensional space. Mapping instead to a density provides many interesting advantages, including better capturing uncertainty about a representation and its relationships, expressing asymmetries more naturally than dot product or cosine similarity, and enabling more expressive parameterization of decision boundaries. This paper advocates for density-based distributed embeddings and presents a method for learning representations in the space of Gaussian distributions. We compare performance on various word embedding benchmarks, investigate the ability of these embeddings to model entailment and other asymmetric relationships, and explore novel properties of the representation.
Date of publication 2014
Code Programming Language Python

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