Uncovering the Corona Virus Map Using Deep Entities and Relationship Models
Published in Arxiv Preprint, 2020
This paper focuses on extracting COVID-19 entities and relationships from a corpus of articles using a novel model. Through multi-task learning on annotated data, we train entity recognition and relationship discovery models. Employing concept masking, we prevent neural networks from functioning as associative memory, promoting contextual inference. Our findings reveal significant subnetworks, emphasize key terms, and illuminate past treatment modalities for related ailments.
Recommended citation: K Singh, P Singla, K Sarode, A Chandrakar and C Nichkawde (2020). “ Uncovering the Corona Virus Map Using Deep Entities and Relationship Models” preprint ArXiv:2009.03068. 1(1).
Recommended citation: K Singh, P Singla, K Sarode, A Chandrakar and C Nichkawde (2020). " Uncovering the Corona Virus Map Using Deep Entities and Relationship Models" preprint ArXiv:2009.03068. 1(1). https://arxiv.org/abs/2009.03068