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This repository implements the multi-digraph model for incorporating gazetteers in Chinese NER. The dataset in the e-commerce domain and 7 NE gazetteers of products and brands are proposed.

Python 3.6, Pytorch >= 0.4, hb-config, dill, tqdm, numpy (or using pip -r requirements.txt)

python -m src.main --config graph_ecommerce

turn on/off the gpu mode in config/graph_ecommerce.yml

remove gazetteers in config/graph_ecommerce.yml to run trivial model (our model - gazetteers = bilstm crf)

To reproduce results in paper, you need to retrain and replace demo embeddings in data/embeddings (including char embedding and bichar embedding trained on Chinese wiki)

Please cite our ACL 2019 paper:

@article{ruixue2019,  
 title={A Neural Multi-digraph Model for Chinese NER with Gazetteers},  
 author={Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li and Luo Si},  
 booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL)},
 year={2019}  
}