  Lexical entailment, such as hyponymy, is a fundamental issue in the semantics
of natural language. This paper proposes distributional semantic models which
efficiently learn word embeddings for entailment, using a recently-proposed
framework for modelling entailment in a vector-space. These models postulate a
latent vector for a pseudo-phrase containing two neighbouring word vectors. We
investigate both modelling words as the evidence they contribute about this
phrase vector, or as the posterior distribution of a one-word phrase vector,
and find that the posterior vectors perform better. The resulting word
embeddings outperform the best previous results on predicting hyponymy between
words, in unsupervised and semi-supervised experiments.
