Global Bootstrapping Neural Network for Entity Set Expansion
Lingyong Yan, Xianpei Han, Ben He, Le Sun · 2020
Bootstrapping for entity set expansion (ESE) has been studied for a long period, which expands new entities using only a few seed entities as supervision.Recent end-to-end bootstrapping approaches have shown their advantages in information capturing and bootstrapping process modeling.However, due to the sparse supervision problem, previous endto-end methods often only leverage information from near neighborhoods (local semantics) rather than those propagated from the co-occurrence structure of the whole corpus (global semantics).To address this issue, this paper proposes Global Bootstrapping Network (GBN) with the "pre-training and fine-tuning" strategies for effective learning.Specifically, it contains a global-sighted encoder to capture and encode both local and global semantics into entity embedding, and an attention-guided decoder to sequentially expand new entities based on these embeddings.The experimental results show that the GBN learned by "pretraining and fine-tuning" strategies achieves state-of-the-art performance on two bootstrapping datasets.