CoRA

Codes and datasets for our paper "Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention"

If you use the code, please cite the following paper:

@article{li2020improving,
  title={Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention},
  author={Li, Yang and Shen, Tao and Long, Guodong and Jiang, Jing and Zhou, Tianyi and Zhang, Chengqi},
  journal={arXiv preprint arXiv:2010.03773},
  year={2020}
}

Requirements

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

• pytorch = 1.4.0
• numpy = 1.19.2
• scipy = 1.5.2

Data preparation

First unzip the ./raw_data/data.zip and put all the files under ./raw_data. Once the original raw text corpus data is in ./raw_data.

Train the model

For CoRA,

python main_CoRA.py --is_training True

Evaluate the model

Run various evaluation by specifying --mode in commandline, see the paper for detailed description for these evaluation methods.

python main_CoRA.py --mode [test method: pr, pone, ptwo, pall, hit_k_100, hit_k_200]

Pretrained models

The pretrained models is already saved at ./outputs/ckpt/.

python main_CoRA.py --mode [test method: pr, pone, ptwo, pall, hit_k_100, hit_k_200] --test_pretrained