CTRL-PG

Codes for paper Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference. Y. Zhou, Y. Yan, R. Han, J. H. Caufield, K. Chang, Y. Sun, P. Ping, W. Wang

Dependencies

Python 3.6
Pytorch 1.0.1+
CUDA 10.0+
torch
numpy
tqdm
transformers=2.2.0
tensorboardX
glob
random
logging
argparse
copy
json
csv

Data

I2B2-2012 dataset and its complete closure evaluating scripts could be downloaded here. More descriptions about the TB-Dense dataset could be found here.

Extracting Temporal relations from TBDense dataset:

cd sources
python run_relation_extraction.py \
     --do_train \
     --do_eval \
     --tbd \
     --evaluate_during_training \
     --do_lower_case \
     --data_dir ../data/tbd/all_context/ \
     --max_seq_length 128 \
     --per_gpu_eval_batch_size=8 \
     --per_gpu_train_batch_size=8 \
     --learning_rate 2e-5 \
     --num_train_epochs 10.0 \
     --output_dir /tmp/tbd \
     --overwrite_output_dir \
     --data_aug triple_rules \
     --aug_round 2 \
     --psllda 0.5

To perform Global inference, two xml file will need for both test and dev data. Gold file should contain the ground truth information of temporal relation. An example is shown below: <TLINK id="TL23" fromID="E107" fromText="a small ventral hernia" toID="E30" toText="This" type="OVERLAP" /> TLINK id is the id of the temporal relation; fromID and toID are the entity IDs; fromText and toText are Text associated with the entity; type is the relation. Similarly, an template xml file is required to write the output, which should have the ID and text information and leave blank for type. --gold_file is the path to dev ground true xml; --xml_folder is the path to dev result xml; --final_xml_folder is the path to test result xml; --test_gold_file is the path to test result xml. --psllda is the parameter that controls the weight of probabilistic soft logic loss introduced.