FILR

Code for paper Document-level Biomedical Relation Extraction Based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning.

DataSet

The CDR and GDA datasets can be downloaded from Google Drive.

File Structure

The expected structure of files is:

ATLOP
 |-- dataset
 |    |-- cdr
 |    |    |-- train_filter.data
 |    |    |-- dev_filter.data
 |    |    |-- test_filter.data
 |    |-- gda
 |    |    |-- train.data
 |    |    |-- dev.data
 |    |    |-- test.data
 |-- saved_model
      |-- best.model
 |-- biobert_base
 |-- utils.py
 |-- adj_utils.py
 |-- prepro.py
 |-- long_seq.py
 |-- losses.py
 |-- train_cdr.py
 |-- train_gda.py
 |-- rgcn.py
 |-- model.py

The biobert_base can be downloaded from Google Drive.

Training and Evaluation

Training

Train CDA and GDA model with the following command:

>> python train_cdr.py  # for CDR
>> python train_gda.py  # for GDA

You can save the model by setting the --save_path argument before training. The model correponds to the best dev results will be saved.

Evaluation

You can download the saved models we reported in paper from Google Drive and place them in --save_path. Then, you can evaluate the saved model by setting the --load_path argument, then the code will skip training and evaluate the saved model on benchmarks.