Chemical-protein Interaction Extraction via ChemicalBERT and Attention Guided Graph Convolutional Networks in Parallel

The model consists of ChemicalBERT and Attention Guided Graph Convolutional Networks (AGGCN) two parallel components. We pre-train BERT on large-scale chemical interaction corpora and re-define it as ChemicalBERT to generate high-quality contextual representation, and employ AGGCN to capture syntactic graph information of the sentence. Finally, the contextual representation and syntactic graph representation are merged into a fusion layer and then fed into the fully-connected softmax layer to extract CPIs.

The paper has been accepted by 2020 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2020).

Conference: December 16-19, 2020

The Program can be found on the Conference Website by clicking Program on the left hand menu.

See below for an overview of the model architecture:

Requirements

• Python 3 (tested on 3.6.10)

• PyTorch (tested on 1.3.1)

• CUDA (tested on 10.1.243)

• pytorch_pretrained_bert (tested on 0.6.1)

• botocore (tested on 1.12.189)

• tensorflow (tested on 1.15.0)

• boto3 (tested on 1.9.162)

• requests (tested on 2.22.0)

• numpy (tested on 1.19.1)

• tqdm (tested on 4.42.1)

Download Resources

• Model config and model weights

Evaluation

we have conducted experiments on the ChemProt corpus and DDIExtraction 2013 corpus

Testing on CPI extraction

python3 eval_cpi.py

Testing on DDI extraction. Before run it, please modify the configuration information under /utils/constant.py

python3 eval_ddi.py