  This paper presents the Bayesian Hierarchical Words Representation (BHWR)
learning algorithm. BHWR facilitates Variational Bayes word representation
learning combined with semantic taxonomy modeling via hierarchical priors. By
propagating relevant information between related words, BHWR utilizes the
taxonomy to improve the quality of such representations. Evaluation of several
linguistic datasets demonstrates the advantages of BHWR over suitable
alternatives that facilitate Bayesian modeling with or without semantic priors.
Finally, we further show that BHWR produces better representations for rare
words.
