Code and dataset of ACL2019 Paper Graph Neural Networks with Generated Parameters for Relation Extraction. paper

Please cite our paper if you find it helpful.

@inproceedings{zhu2019graph,
  title={Graph Neural Networks with Generated Parameters for Relation Extraction},
  author={Zhu, Hao and Lin, Yankai and Liu, Zhiyuan and Fu, Jie and Chua, Tat-Seng and Sun, Maosong},
  booktitle={Proceedings of ACL},
  year={2019}
}

This repository is mainly contributed by Hao Zhu, Chaojun Xiao. And this repository is developed based on ContextAware.

Please download gpgnn_data.zip from Aliyun and unzip it as the data folder:

unzip gpgnn_data.zip

This repository has been tested with Python 3.6,torch==1.4.0,sacred==0.8.1

pip3 install -r requirements.txt

models/: Baseline models (LSTM, CNN, PCNN, ContextAware) in baselines.py and GPGNN model in our_models.py.

parsing/: APIs to convert graphs into tensors which can be fed into our models.

semanticgraph/: APIs to construct relation graphs from sentences.

utils/: APIs to load word embeddings, evaluate, and operate the graphs.

result/: Storage area for models and output results on the test set.

model_param.json: Hyper parameters for the GPGNN model.

train:

The parameters of the function main are set in function main_config. You can modify the parameters in main_config to train different models. And the hyper parameters for the models are stored in model_params.json.

python3 train.py

test:

Before testing, you should choose proper version of the model by modifing the load_model in main_config.

python3 test.py

evaluate:

cd result
python3 test_accuracy.py --input (result_file_path)