@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://aclanthology.org/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."
}
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<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 “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.</abstract>
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%0 Conference Proceedings
%T Global Bootstrapping Neural Network for Entity Set Expansion
%A Yan, Lingyong
%A Han, Xianpei
%A He, Ben
%A Sun, Le
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F yan-etal-2020-global
%X 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.
%R 10.18653/v1/2020.findings-emnlp.331
%U https://aclanthology.org/2020.findings-emnlp.331/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.331
%P 3705-3714
Markdown (Informal)
[Global Bootstrapping Neural Network for Entity Set Expansion](https://aclanthology.org/2020.findings-emnlp.331/) (Yan et al., Findings 2020)
ACL