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Title: BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision
We study the open-domain named entity recognition (NER) prob- lem under distant supervision. The distant supervision, though does not require large amounts of manual annotations, yields highly in- complete 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 algo- rithm: 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 bench- mark 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.  more » « less
Award ID(s):
1717916
NSF-PAR ID:
10162617
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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