  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 that enforces two constraints: (a) for both source and
target domain documents, pivots that appear in a document must accurately
predict the co-occurring non-pivots, and (b) word representations learnt for
pivots must be similar in the two domains. Moreover, we propose a method to
perform domain adaptation using the learnt word representations. Our proposed
method significantly outperforms competitive baselines including the
state-of-the-art domain-insensitive word representations, and reports best
sentiment classification accuracies for all domain-pairs in a benchmark
dataset.
