  Bootstrapping has become the mainstream method for entity set expansion.
Conventional bootstrapping methods mostly define the expansion boundary using
seed-based distance metrics, which heavily depend on the quality of selected
seeds and are hard to be adjusted due to the extremely sparse supervision. In
this paper, we propose BootstrapGAN, a new learning method for bootstrapping
which jointly models the bootstrapping process and the boundary learning
process in a GAN framework. Specifically, the expansion boundaries of different
bootstrapping iterations are learned via different discriminator networks; the
bootstrapping network is the generator to generate new positive entities, and
the discriminator networks identify the expansion boundaries by trying to
distinguish the generated entities from known positive entities. By iteratively
performing the above adversarial learning, the generator and the discriminators
can reinforce each other and be progressively refined along the whole
bootstrapping process. Experiments show that BootstrapGAN achieves the new
state-of-the-art entity set expansion performance.
