@article{Han_Sun_Zhang_Li_Shi_2020, title={CASE: Context-Aware Semantic Expansion}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/6293}, DOI={10.1609/aaai.v34i05.6293}, abstractNote={<p>In this paper, we define and study a new task called <em>Context-Aware Semantic Expansion</em> (CASE). Given a <em>seed term</em> 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 <span style="text-decoration: underline;">at scale</span> 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.</p>}, number={05}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Han, Jialong and Sun, Aixin and Zhang, Haisong and Li, Chenliang and Shi, Shuming}, year={2020}, month={Apr.}, pages={7871-7878} }