Hierarchical Density Order Embeddings

ICLR 2018 Ben AthiwaratkunAndrew Gordon Wilson

By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. The uncertainty information can be particularly meaningful in capturing entailment relationships -- whereby general words such as "entity" correspond to broad distributions that encompass more specific words such as "animal" or "instrument"... (read more)

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