Collecting Targeted Information About Covid-19 From Research Papers By Asking Questions Based On Natural Language Processing

In the general framework of knowledge discovery,
different techniques were used for information extraction
from multi-label documents. As the world is currently facing
COVID-19, it has made it more important than ever to have
such knowledge extraction from previous documents.
Therefore, Natural Language Processing (NLP) can be an
essential model for tackling such an issue. By taking into
consideration that having such a model plays an essential
role to generate new insights in support of the ongoing fight
against this infectious disease. This work introduces a
sophisticated model that is able to read data from various
articles about COVID-19, and finally give the most
appropriate answer to the questions asked in order to gain
insight information automatically. The model is applied to
COVID-19 open research dataset challenge (CORD-19)
that’s has caught the attention of many researchers and it
contains over 400,000 scholarly articles. The result of the
proposed model has shown a good achievement, as it is
explained in the result section. It was found that NLP is a
good choice for tackling this global pandemic for
information extraction and it contribute a new insight in
support of the ongoing fight against this infectious disease.