BioEGRE: A Graph Pointer Neural Network based Method for Biomedical Relation Extraction

Additional Requirements

• sklearn: Used for RE evaluation (pip install scikit-learn)
• pandas : Used for RE evaluation (pip install pandas)

Training

export SAVE_DIR=/tmp/output
export DATA="GAD"
export SPLIT="1"
export CLASSIFIER_DROPOUT=0.0
export DATA_DIR=/tmp/zhengxw/${DATA}/${SPLIT}

export MAX_LENGTH=128
export BATCH_SIZE=8
export NUM_EPOCHS=20
export SAVE_STEPS=1000
export SEED=1
export NUM_CHOSN_NEIGHBORS=32
export NUM_GPNN_OUTPUT_NODE=4
export USE_CLS=False
export NUM_GPNN_LAYERS=0-0

export ENTITY=${DATA}-${SPLIT}-${MAX_LENGTH}-${BATCH_SIZE}-${NUM_EPOCHS}-${NUM_CHOSN_NEIGHBORS}-${NUM_GPNN_OUTPUT_NODE}-${CLASSIFIER_DROPOUT}-${USE_CLS}-${NUM_GPNN_LAYERS}

export MODEL=BioELECTRA

python bioelectra_linking_CLS_with_entities_with_marked_word_with_multiple_gpnn_layers_for_binary_classification.py \
    --task_name gad \
    --config_name ./bioelectra-base-discriminator-pubmed/config.json \
    --data_dir ${DATA_DIR} \
    --num_chosn_neighbors ${NUM_CHOSN_NEIGHBORS}\
    --num_GPNN_output_node ${NUM_GPNN_OUTPUT_NODE}\
    --model_name_or_path ./bioelectra-base-discriminator-pubmed \
    --max_seq_length ${MAX_LENGTH} \
    --num_train_epochs ${NUM_EPOCHS} \
    --per_device_train_batch_size ${BATCH_SIZE} \
    --save_steps ${SAVE_STEPS} \
    --seed ${SEED} \
    --do_predict \
    --learning_rate 5e-5 \
    --output_dir ${SAVE_DIR}/${ENTITY}-${MODEL} \
    --overwrite_output_dir \
    --classifier_dropout ${CLASSIFIER_DROPOUT} \
    --logging_dir ./logs/${ENTITY}-${MODEL} \
    --logging_steps 20\
    --dataloader_pin_memory True\
    --dataloader_num_worker 8

Evaluation

python ./scripts/re_eval.py --output_path=${SAVE_DIR}/test_results.txt --answer_path=${DATA_DIR}/test.tsv

To evaluate the prediction, please use scripts/re_eval.py file. For an example running script for 10-cv experiment, please task a look at run_re_10cv.sh and scripts/re_eval_10cv.sh.

Evaluation Results

BioEGRE

	Precision (%)	Recall (%)	F1 (%)
GAD	79.21	87.11	82.98
EUADR	81.14	84.98	83.00

Contact

For help or issues, please contact xwzheng60602@163.com .