  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.
