1. [Publications](/index.php/publications)
2. Text Mining Drug/Chemical-Protein Interactions using an Ensemble of BERT and T5 Based Models
 
 # Text Mining Drug/Chemical-Protein Interactions using an Ensemble of BERT and T5 Based Models

  ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image")

 In Track-1 of the BioCreative VII Challenge participants are asked to identify interactions between drugs/chemicals and proteins. In-context named entity annotations for each drug/chemical and protein are provided and one of fourteen different interactions must be automatically predicted. For this relation extraction task, we attempt both a BERT-based sentence classification approach, and a more novel text-to-text approach using a T5 model. We find that larger BERT-based models perform better in general, with our BioMegatron-based model achieving the highest scores across all metrics, achieving 0.74 F1 score. Though our novel T5 text-to-text method did not perform as well as most of our BERT-based models, it outperformed those trained on similar data, showing promising results, achieving 0.65 F1 score. We believe a text-to-text approach to relation extraction has some competitive advantages and there is a lot of room for research advancement.



 ## Authors



Virginia Adams (NVIDIA)

Hoo-Chang Shin (NVIDIA)

Carol Anderson (NVIDIA)

Bo Liu (NVIDIA)

Anas Abidin (NVIDIA)

 

 

 ## Publication Date



Tuesday, November 30, 2021

 

 ## Research Area



[Natural Language Processing](/index.php/research-area/natural-language-processing)

 

 

 ## External Links



[Paper](https://arxiv.org/abs/2111.15617)