Codes and datasets for our paper "Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention"
If you use the code, please cite the following paper:
@inproceedings{han2018hierarchicalRE,
title={Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention},
author={Han, Xu and Yu, Pengfei and Liu, Zhiyuan and Sun, Maosong and Li, Peng},
booktitle={Proceedings of EMNLP},
year={2018}
}
The model is implemented using tensorflow. The versions of packages used are shown below.
- tensorflow = 1.4.1
- numpy = 1.13.3
- scipy = 0.19.1
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, run
python scripts/initial.py
For CNN hierarchical model,
PYTHONPATH=. python scripts/train.py --model cnn_hier
For PCNN,
PYTHONPATH=. python scripts/train.py --model pcnn_hier
Run various evaluation by specifying --mode in commandline, see the paper for detailed description for these evaluation methods.
PYTHONPATH=. python scripts/evaluate.py --mode [test method: pr, pone, ptwo, pall] --test-single --test_start_ckpt [ckpt number to be tested] --model [cnn_hier or pcnn_hier]
The logits are saved at ./outputs/logits/. To see the PR curve, run the following command which directly show() the curve, and you can adjust the codes in ./scripts/show_pr.py for saving the image as pdf file or etc. :
python scripts/show_pr.py [path/to/generated .npy logits file from evaluation]
The pretrained models is already saved at ./outputs/ckpt/. To directly evaluate on them, run the following command:
PYTHONPATH=. python scripts/evaluate.py --mode [test method: pr, pone, ptwo, pall] --test-single --test_start_ckpt 0 --model [cnn_hier or pcnn_hier]
And PR curves can be generated same way as above.
Some of other baselines can be found in other baselines.