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}. Next, we optimize an objective function …

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}. Next, we optimize an objective function …