Unsupervised Cross-Domain Word Representation Learning

Understand

Meaning of a word varies from one domain to another.

• Despite this important domain dependence in word semantics, existing word representation learning methods are bound to a single domain.
• Given a pair of \emph{source}-\emph{target} domains, we propose an unsupervised method for learning domain-specific word representations that accurately capture the domain-specific aspects of word semantics.
• First, we select a subset of frequent words that occur in both domains as \emph{pivots}.