Uncovering the Corona Virus Map Using Deep Entities and Relationship Models

TL;DR

This paper presents a novel deep learning approach to extract entities and relationships related to COVID-19 from scientific articles, uncovering key subnetworks and concepts to aid understanding of the virus and potential treatments.

Contribution

It introduces a multi-task learning model with concept masking to improve entity and relationship extraction specific to COVID-19 literature.

Findings

Identified important subnetworks and key terms related to COVID-19

Highlighted treatment modalities from past related ailments

Demonstrated effectiveness of the proposed deep learning model

Abstract

We extract entities and relationships related to COVID-19 from a corpus of articles related to Corona virus by employing a novel entities and relationship model. The entity recognition and relationship discovery models are trained with a multi-task learning objective on a large annotated corpus. We employ a concept masking paradigm to prevent the evolution of neural networks functioning as an associative memory and induce right inductive bias guiding the network to make inference using only the context. We uncover several import subnetworks, highlight important terms and concepts and elucidate several treatment modalities employed in related ailments in the past.