[KDD 2020] BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision
Aug 13, 202011 views
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.