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Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition (CIKM2023)

This repository contains all of our source code. We sincerely thank the help of Zheng et al.'s repository.

Overview of the directory

• bert-base-cased/: the directory of configurations and PyTorch pretrained model for bert-base-cased
• config/ : the directory of configurations for our CPFD method
• datasets/ : the directory of datasets
• experiments/ : the directory of training logs from different runs
• src/ : the directory of the source code
• main_CL.py : the python file to be executed

.
├── bert-base-cased
├── config
│   ├── conll2003
│   ├── ontonotes5
│   ├── i2b2
├── datasets
│   └── NER_data
│       ├── conll2003
│       ├── i2b2
│       └── ontonotes5
├── experiments
│   └── result_analyze.py
|   └── xxx.pth
├── main_CL.py
└── src
    ├── config.py
    ├── dataloader.py
    ├── model.py
    ├── trainer.py
    ├── utils_plot.py
    └── utils.py

Step 1: Prepare your environments

Reference environment settings:

python             3.7.13
torch              1.12.1+cu116
transformers       4.14.1

Download bert-base-cased to the directory of bert-base-cased/

Download base models to the directory of experiments/

Step 2: Run main_CL.py

Specify your configurations (e.g., ./config/i2b2/fg_8_pg_2/RDP.yaml) and run the following command

CUDA_VISIBLE_DEVICES=0 nohup python3 -u main_CL.py --exp_name i2b2_8-2_RDP --exp_id 1 --cfg config/i2b2/fg_8_pg_2/RDP.yaml 2>&1 &

Then, the results as well as the model checkpoint will be saved automatically in the directory ./experiments/i2b2_8-2_RDP/1/

Citation

@inproceedings{zhang2023rdp,
  title={Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition},
  author={Zhang, Duzhen and Li, Hongliu and Cong, Wei and Xu, Rongtao and Dong, Jiahua and Chen, Xiuyi},
  booktitle={Proceedings of the 32th ACM International Conference on Information \& Knowledge Management},
  year={2023}
}