ToNER

If you find our work useful for your research, please cite the following paper ToNER:

@inproceedings{jiang2024toner,
  title={ToNER: Type-oriented Named Entity Recognition with Generative Language Model},
  author={Jiang, Guochao and Luo, Ziqin and Shi, Yuchen and Wang, Dixuan and Liang, Jiaqing and Yang, Deqing},
  booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  pages={16251--16262},
  year={2024}
}

Environment

Create a new Python virtual environment for ToNER:

conda create -n toner python=3.8
conda activate toner
pip install -r requirements.txt

Data Preparation

Please save your dataset in data folder.

• CoNLL2003: https://huggingface.co/datasets/conll2003
• OntoNotes 5.0: https://huggingface.co/datasets/tner/ontonotes5
• JNLPBA: https://huggingface.co/datasets/jnlpba
• ACE2004: https://catalog.ldc.upenn.edu/LDC2005T09
• ACE2005: https://catalog.ldc.upenn.edu/LDC2006T06

Train

Type Matching Model

Please review flat_recall_train.py when you want to use ToNER in flat NER dataset or nest_recall_train.py when you want to use ToNER in nest NER dataset, and change some import parameters.

python flat_recall_train.py
python nest_recall_train.py

Data Generation

Please review data/flat_cls.py for ToNER in flat NER dataset, data/flat_exp.py for ToNER-EXP in flat NER dataset, data/nest_cls.py for ToNER in nest NER dataset, data/nest_exp.py for ToNER-EXP in nest NER dataset, and change some import parameters.

python data/flat_cls.py
python data/flat_exp.py
python data/nest_cls.py
python data/nest_exp.py

Train ToNER

Please review main.py and config.yaml for your ToNER training, and change some import parameters.

accelerate launch --config_file config.yaml --debug main.py