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KDD 2020 | BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision

Accepted Papers

BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision

Chen Liang: Georgia Institute of Technology; Yue Yu: Georgia Institute of Technology; Haoming Jiang: Georgia Institute of Technology; Siawpeng Er: Georgia Institute of Technology; Ruijia Wang: Georgia Institute of Technology; Tuo Zhao: Georgia Institute of Technology; Chao Zhang: Georgia Institute of Technology


We study the open-domain named entity recognition (NER) problem under distant supervision. The distant supervision, though does not require large amounts of manual annotations, yields highly incomplete and noisy distant labels via external knowledge bases. To address this challenge, we propose a new computational framework—BOND, which leverages the power of pre-trained language models (e.g., BERT and RoBERTa) to improve the prediction performance of NER models. Specifically, we propose a two-stage training algorithm: In the first stage, we adapt the pre-trained language model to the NER tasks using the distant labels, which can significantly improve the recall and precision; In the second stage, we drop the distant labels, and propose a self-training approach to further improve the model performance. Thorough experiments on 5 benchmark datasets demonstrate the superiority of BOND over existing distantly supervised NER methods. The code and distantly labeled data have been released in https://github.com/cliang1453/BOND.

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