Papers › Global Bootstrapping Neural Network for Entity Set Expansion
Global Bootstrapping Neural Network for Entity Set Expansion
Lingyong Yan, Xianpei Han, Ben He, Le Sun
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 end-to-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 {``}pre-training and fine-tuning{''} strategies achieves state-of-the-art performance on two bootstrapping datasets.
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