  The meaning of a word often varies depending on its usage in different
domains. The standard word embedding models struggle to represent this
variation, as they learn a single global representation for a word. We propose
a method to learn domain-specific word embeddings, from text organized into
hierarchical domains, such as reviews in an e-commerce website, where products
follow a taxonomy. Our structured probabilistic model allows vector
representations for the same word to drift away from each other for distant
domains in the taxonomy, to accommodate its domain-specific meanings. By
learning sets of domain-specific word representations jointly, our model can
leverage domain relationships, and it scales well with the number of domains.
Using large real-world review datasets, we demonstrate the effectiveness of our
model compared to state-of-the-art approaches, in learning domain-specific word
embeddings that are both intuitive to humans and benefit downstream NLP tasks.
