Uncovering the Corona Virus Map Using Deep Entities and Relationship Models
This work addresses the need for structured information extraction from COVID-19 literature, but it appears incremental as it builds on existing entity-relationship models with specific adaptations.
The researchers tackled the problem of extracting COVID-19-related entities and relationships from articles by using a novel model with multi-task learning and concept masking, resulting in the identification of subnetworks, key terms, and past treatment modalities.
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.