@inproceedings{yan-etal-2020-global,
    title = "Global Bootstrapping Neural Network for Entity Set Expansion",
    author = "Yan, Lingyong  and
      Han, Xianpei  and
      He, Ben  and
      Sun, Le",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.findings-emnlp.331/",
    doi = "10.18653/v1/2020.findings-emnlp.331",
    pages = "3705--3714",
    abstract = "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 {\textquotedblleft}pre-training and fine-tuning{\textquotedblright} 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 {\textquotedblleft}pre-training and fine-tuning{\textquotedblright} strategies achieves state-of-the-art performance on two bootstrapping datasets."
}