BOND

This repo contains our code and pre-processed distantly/weakly labeled data for paper BOND: BERT-Assisted Open-Domain Name Entity Recognition with Distant Supervision (KDD2020)

BOND

Benchmark

The reuslts (entity-level F1 score) are summarized as follows:

Method	CoNLL03	Tweet	OntoNote5.0	Webpage	Wikigold
Full Supervision	91.21	52.19	86.20	72.39	86.43
Previous SOTA	76.00	26.10	67.69	51.39	47.54
BOND	81.48	48.01	68.35	65.74	60.07

• Full Supervision: Roberta Finetuning/BiLSTM CRF
• Previous SOTA: BiLSTM-CRF/AutoNER/LR-CRF/KALM/CONNET

Data

We release five open-domain distantly/weakly labeled NER datasets here: dataset. For gazetteers information and distant label generation code, please directly email [email protected].

Environment

Python 3.7, Pytorch 1.3, Hugging Face Transformers v2.3.0.

Training & Evaluation

We provides the training scripts for all five open-domain distantly/weakly labeled NER datasets in scripts. E.g., for BOND training and evaluation on CoNLL03

cd BOND
./scripts/conll_self_training.sh

For Stage I training and evaluation on CoNLL03

cd BOND
./scripts/conll_baseline.sh

The test reuslts (entity-level F1 score) are summarized as follows:

Method	CoNLL03	Tweet	OntoNote5.0	Webpage	Wikigold
Stage I	75.61	46.61	68.11	59.11	52.15
BOND	81.48	48.01	68.35	65.74	60.07

Citation

Please cite the following paper if you are using our datasets/tool. Thanks!

@inproceedings{liang2020bond,
  title={BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision},
  author={Liang, Chen and Yu, Yue and Jiang, Haoming and Er, Siawpeng and Wang, Ruijia and Zhao, Tuo and Zhang, Chao},
  booktitle={ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year={2020}
}