Awesome

HNRE

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}
 }

Requirements

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

Initialization

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

Train the model

For CNN hierarchical model,

PYTHONPATH=. python scripts/train.py --model cnn_hier

For PCNN,

PYTHONPATH=. python scripts/train.py --model pcnn_hier

Evaluate the model

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]

Pretrained models

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: hit_k_100, hit_k_200, 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.

The results of the released checkpoints

As this toolkit is reconstructed based on the original code and the checkpoints are retrained on this toolkit, the results of the released checkpoints are comparable with the reported ones.

The Main Experiments

Model	Micro	Macro
CNN+HATT	41.8	16.5
PCNN+HATT	42.0	17.1

The Auxiliary Experiments

Hits@N(<100)

micro	10	15	20
CNN+HATT	5.6	33.3	50.0
PCNN+HATT	33.3	50.0	61.1

macro	10	15	20
CNN+HATT	5.6	29.6	57.4
PCNN+HATT	29.6	50.0	61.1

Hits@N(<200)

micro	10	15	20
CNN+HATT	41.4	58.6	69.0
PCNN+HATT	55.2	69.0	75.9

macro	10	15	20
CNN+HATT	22.7	42.4	65.2
PCNN+HATT	41.4	59.1	68.2

pone

P@N	100	200	300	Mean
CNN+HATT	84.0	76.5	70.7	77.1
PCNN+HATT	82.0	75.0	71.0	76.0

ptwo

P@N	100	200	300	Mean
CNN+HATT	83.0	79.0	72.3	78.1
PCNN+HATT	82.0	77.0	75.3	78.1

pall

P@N	100	200	300	Mean
CNN+HATT	82.0	80.0	74.7	78.9
PCNN+HATT	81.0	80.5	76.0	79.2

Baseline models

+ATT,+ONE

+ADV

+SL

Some of other baselines can be found in other baselines.