E2EMERN

[Title] An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization

[Authors] Baohang Zhou, Xiangrui Cai, Ying Zhang, Xiaojie Yuan

ACL 2021 paper

Preparation

1. Clone the repo to your local.
2. Download Python version: 3.6.5.
3. Download the pre-trained Bio-BERT models from this link. We use the BioBERT-Large in our experiments.
4. Open the shell or cmd in this repo folder. Run this command to install necessary packages.

pip install -r requirements.txt

Experiments

1. For Linux systems, we have shell scripts to run the training procedures. You can run the following command:

./train.ncbi.sh or ./train.bc5cdr.sh

2. You can also input the following command to train the model. There are different choices for some parameters shown in square brackets. The meaning of these parameters are shown in the following tables.

Parameters	Value	Description
epoch	int	Training times
LAMBDA	float	hyper-parameter in loss function
MU	float	hyper-parameter in loss function
bert_path	str	folder path of pre-trained BERT model
save_pred_result	bool	save the prediction result

python main.py \
    --seed 11 \
    --epoch 12 \
    --LAMBDA 0.125 \
    --MU 0.1 \
    --dataset [ncbi, cdr] \
    --bert_path ./biobert_large \
    --save_pred_result \

3. After training the model, the test result is saved in the "results" folder. And the weights of the model are saved in the "weights" folder.

4. We also provide the weights of the model to reimplement the results in our paper. You can download the weights file (the extraction code 1234) and put them into the "weights" folder. Then run the following command:

./eval.ncbi.sh or ./eval.bc5cdr.sh

Bibtex:

@inproceedings{zhou-etal-2021-end,
    title = "An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization",
    author = "Zhou, Baohang  and
      Cai, Xiangrui  and
      Zhang, Ying  and
      Yuan, Xiaojie",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.485",
    pages = "6214--6224",
}