Tensorflow Implementation of Convolutional Neural Network for Relation Extraction (COLING 2014, NAACL 2015)
Tensorflow Implementation of Deep Learning Approach for Relation Extraction Challenge(SemEval-2010 Task #8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals) via Convolutional Neural Networks.
$ python train.py --help
$ python train.py --embedding_path "GoogleNews-vectors-negative300.bin"
--checkpoint_dir
ARGUMENT, checkpoint directory from training run, like below example.$ python eval.py --checkpoint_dir "runs/1523902663/checkpoints/"
SemEval-2010 Task #8 Dataset [Download]
Relation | Train Data | Test Data | Total Data |
---|---|---|---|
Cause-Effect | 1,003 (12.54%) | 328 (12.07%) | 1331 (12.42%) |
Instrument-Agency | 504 (6.30%) | 156 (5.74%) | 660 (6.16%) |
Product-Producer | 717 (8.96%) | 231 (8.50%) | 948 (8.85%) |
Content-Container | 540 (6.75%) | 192 (7.07%) | 732 (6.83%) |
Entity-Origin | 716 (8.95%) | 258 (9.50%) | 974 (9.09%) |
Entity-Destination | 845 (10.56%) | 292 (10.75%) | 1137 (10.61%) |
Component-Whole | 941 (11.76%) | 312 (11.48%) | 1253 (11.69%) |
Member-Collection | 690 (8.63%) | 233 (8.58%) | 923 (8.61%) |
Message-Topic | 634 (7.92%) | 261 (9.61%) | 895 (8.35%) |
Other | 1,410 (17.63%) | 454 (16.71%) | 1864 (17.39%) |
Total | 8,000 (100.00%) | 2,717 (100.00%) | 10,717 (100.00%) |