GHE-LPC

Source codes for the paper "Distantly Supervised Relation Extraction using Global Hierachy Embeddings and Local Probability Constraints" from Knowledge-Based Systems .

Requirements

The model is implemented using Pytorch. The versions of packages used are shown below.

• python==3.7.9
• pytorch==1.7.1
• cuda==10.1.243
• numpy==1.19.2
• tqdm==4.55.1
• scikit_learn==0.23.2

Data

First unzip the dataset ./raw_data/data.zip under ./data/.

unzip ./raw_data/data.zip -d ./data

Train the model

python main.py [--model GHE-LPC --processed_data_dir _processed_data/GHE-LPC/ --use_ghe --use_lpc --save_dir result/GHE-LPC/]

Evaluate the model

python evaluate.py [--model GHE-LPC --processed_data_dir ./_processed_data/GHE-LPC/ --use_ghe --use_lpc  --save_dir ./result/GHE-LPC/ --pone --ptwo --pall]

Use pretrained model

The pretrained model is already saved at ./result/checkpoint/. To directly evaluate on it, run the following command:

python evaluate.py --model GHE-LPC --processed_data_dir ./_processed_data/GHE-LPC/ --use_ghe --use_lpc  --save_dir ./result/checkpoint/ --pone --ptwo --pall

P-R curve, the curve will be saved at ./result/checkpoint/:

python show_pr.py GHE-LPC

Experiment results

P-R curve

AUC & Max_F1

AUC	Max_F1
0.561	0.549

P@N (all sentences bags)

P@100	P@200	P@300	P@500	P@1000	P@2000	Mean
94.0	94.0	91.7	85.4	69.9	54.0	81.5

P@N (all non-single-sentence bags)

• pone

P@100	P@200	P@300	Mean
97.0	94.0	88.7	93.2

• ptwo

P@100	P@200	P@300	Mean
98.0	95.5	90.3	94.6

• pall

P@100	P@200	P@300	Mean
98.0	96.5	92.3	95.6

Acknowledgements

We thank a lot for the following repos:

• thunlp/HNRE
• tmliang/SeG
• Diego999/pyGAT