  Background: The COVID-19 pandemic has caused severe impacts on health systems
worldwide. Its critical nature and the increased interest of individuals and
organizations to develop countermeasures to the problem has led to a surge of
new studies in scientific journals. Objetive: We sought to develop a tool that
incorporates, in a novel way, aspects of Information Retrieval (IR) and
Extraction (IE) applied to the COVID-19 Open Research Dataset (CORD-19). The
main focus of this paper is to provide researchers with a better search tool
for COVID-19 related papers, helping them find reference papers and hightlight
relevant entities in text. Method: We applied Latent Dirichlet Allocation (LDA)
to model, based on research aspects, the topics of all English abstracts in
CORD-19. Relevant named entities of each abstract were extracted and linked to
the corresponding UMLS concept. Regular expressions and the K-Nearest Neighbors
algorithm were used to rank relevant papers. Results: Our tool has shown the
potential to assist researchers by automating a topic-based search of CORD-19
papers. Nonetheless, we identified that more fine-tuned topic modeling
parameters and increased accuracy of the research aspect classifier model could
lead to a more accurate and reliable tool. Conclusion: We emphasize the need of
new automated tools to help researchers find relevant COVID-19 documents, in
addition to automatically extracting useful information contained in them. Our
work suggests that combining different algorithms and models could lead to new
ways of browsing COVID-19 paper data.
