CP-GCN

This is the implementation of Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest at COLING 2022.

You can download the datasets and our pre-trained model in here.

The code includes two datasets: CPR and PGR, both of them is avilable in ./dataset. The PubMed dataset is available in https://github.com/Cartus/AGGCN/tree/master/PubMed.

Requirements

• Python 3 (tested on 3.8.8)

• PyTorch (tested on 1.10.1)

• CUDA (tested on 11.4)

• tqdm, networkx

• unzip, wget (for downloading only)

Preparation

First, download and unzip GloVe vectors:

sh download.sh

Then prepare vocabulary and initial word vectors for different datasets (cpr/pgr). Take CPR as an example:

python prepare_vocab.py dataset/cpr dataset/cpr --glove_dir dataset/glove

This will write vocabulary and word vectors as a numpy matrix into their corresponding dir ./dataset/cpr.

Training a task-specific explainer

We have released the causal explanation dataset for our cpr and pgr dataset in ./distillation/dataset/. To train the task-specific causal explainer, run:

sh training_explainer.sh

Model will be saved to ./explanation/cpr_top20.

We have released the trained task-specific causal explainer in ./explanation/cpr_top20_old/

Training CP-GCN

To train the CP-GCN model, run:

sh train_cpr.sh

Model checkpoints and logs will be saved to ./saved_models/cpr.

Evaluation for CP-GCN

Our pre-trained model is saved under the dir ./saved_models/cpr. To run evaluation on the test set, run:

python eval.py saved_model/cpr

Citation

@article{jin2022supporting,
  title={Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest},
  author={Jin, Yifan and Li, Jiangmeng and Lian, Zheng and Jiao, Chengbo and Hu, Xiaohui},
  journal={arXiv preprint arXiv:2208.13472},
  year={2022}
}