  Structured representations of entity names are useful for many entity-related
tasks such as entity normalization and variant generation. Learning the
implicit structured representations of entity names without context and
external knowledge is particularly challenging. In this paper, we present a
novel learning framework that combines active learning and weak supervision to
solve this problem. Our experimental evaluation show that this framework
enables the learning of high-quality models from merely a dozen or so labeled
examples.
