CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles

Zubair Afzal, Vikrant Yadav, Olga Fedorova, Vaishnavi Kandala, Janneke van de Loo, Saber A Akhondi, Pascal Coupet, George Tsatsaronis

Keywords: Covid19, Covid-19 Relevancy Algorithm, Core Covid Articles, Covid Relevancy Framework, Deep Active Learning

Abstract: Ever since the COVID-19 pandemic broke out, the academic and scientific research community, as well as industry and governments around the world have joined forces in an unprecedented manner to fight the threat. Clinicians, biologists, chemists, bioinformaticians, nurses, data scientists, and all of the affiliated relevant disciplines have been mobilized to help discover efficient treatments for the infected population, as well as a vaccine solution to prevent further the virus’ spread. In this combat against the virus responsible for the pandemic, key for any advancements is the timely, accurate, peer-reviewed, and efficient communication of any novel research findings. In this paper we present a novel framework to address the information need of filtering efficiently the scientific bibliography for relevant literature around COVID-19. The contributions of the paper are summarized in the following: we define and describe the information need that encompasses the major requirements for COVID-19 articles’ relevancy, we present and release an expert-curated benchmark set for the task, and we analyze the performance of several state-of-the-art machine learning classifiers that may distinguish the relevant from the non-relevant COVID-19 literature.