LGN

Pytorch implementation of A Lexicon-Based Graph Neural Network for Chinese NER.

The code is partially referred to https://github.com/jiesutd/LatticeLSTM.

Requirements

• Python 3.6 or higher
• Pytorch 0.4.1 or higher

Input Format

BMES tag scheme, with each character its label for one line. Sentences are splited with a null line.

印   B-LOC
度   M-LOC
河   E-LOC
流   O
经   O
印   B-GPE
度   E-GPE

Usage

• Training

    python main.py --status train \
                   --train data/onto4ner.cn/train.char.bmes \
                   --dev data/onto4ner.cn/dev.char.bmes \
                   --test data/onto4ner.cn/test.char.bmes \
                   --saved_model saved_model/model_onto4ner \
                   --saved_set data/onto4ner.cn/saved.dset
  

• Testing

    python main.py --status test \
                   --test data/onto4ner.cn/test.char.bmes \
                   --saved_model saved_model/model_onto4ner \
                   --saved_set data/onto4ner.cn/saved.dset
  

• Decoding (Raw file can either be labeled or not.)

    python main.py --status decode \
                   --raw data/onto4ner.cn/test.char.bmes \
                   --output tagged_file.txt \
                   --saved_model saved_model/model_onto4ner \
                   --saved_set data/onto4ner.cn/saved.dset
  

Data

The pretrained character and word embeddings can be downloaded from Lattice LSTM.

Original datasets can be found at OntoNotes, MSRA, Weibo and Resume. If you want preprocessed datasets that satisfy the input format of our codes, please contact me.

Pretrained Model Downloads

We also provide pretrained models on the four datasets, which are the same models as reported in the paper. If you try to retrain models from scratch under the same hyper-parameter settings, you may obtain a sightly lower or higher F1 score than that reported in the paper (in our experiments we selected the model that performed best).

Pretrained models and related hyper-parameter settings are available at Google Drive or Baidu Pan.

When running main.py in test mode for pretrained models, you can get the results as follows:

Citation

@inproceedings{gui2019lexicon,
 title={A Lexicon-Based Graph Neural Network for Chinese NER},
 author={Gui, Tao and Zou, Yicheng and Zhang, Qi and Peng, Minlong and 
 Fu, Jinlan and Wei, Zhongyu and Huang, Xuanjing},
 booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing 
 and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
 pages={1039--1049},
 year={2019}
}