  Information resources such as newspapers have produced unstructured text data
in various languages related to the corona outbreak since December 2019.
Analyzing these unstructured texts is time-consuming without representing them
in a structured format; therefore, representing them in a structured format is
crucial. An information extraction pipeline with essential tasks -- named
entity tagging and relation extraction -- to accomplish this goal might be
applied to these texts. This study proposes a data annotation pipeline to
generate training data from corona news articles, including generic and
domain-specific entities. Named entity recognition models are trained on this
annotated corpus and then evaluated on test sentences manually annotated by
domain experts evaluating the performance of a trained model. The code base and
demonstration are available at https://github.com/sefeoglu/coronanews-ner.git.
