  In this paper, we define and study a new task called Context-Aware Semantic
Expansion (CASE). Given a seed term in a sentential context, we aim to suggest
other terms that well fit the context as the seed. CASE has many interesting
applications such as query suggestion, computer-assisted writing, and word
sense disambiguation, to name a few. Previous explorations, if any, only
involve some similar tasks, and all require human annotations for evaluation.
In this study, we demonstrate that annotations for this task can be harvested
at scale from existing corpora, in a fully automatic manner. On a dataset of
1.8 million sentences thus derived, we propose a network architecture that
encodes the context and seed term separately before suggesting alternative
terms. The context encoder in this architecture can be easily extended by
incorporating seed-aware attention. Our experiments demonstrate that
competitive results are achieved with appropriate choices of context encoder
and attention scoring function.
