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Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion
[article]
2021
arXiv
pre-print
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,
arXiv:2109.12082v1
fatcat:inuocx3sqrfkddv3lmtwv4jmuq