Word Representations via Gaussian Embedding

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… (More)
View Slides

Topics

Statistics

0502015201620172018
Citations per Year

119 Citations

Semantic Scholar estimates that this publication has 119 citations based on the available data.

See our FAQ for additional information.

  • Blog articles referencing this paper

  • Presentations referencing similar topics