  We created this CORD-NER dataset with comprehensive named entity recognition
(NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus
(2020-03-13). This CORD-NER dataset covers 75 fine-grained entity types: In
addition to the common biomedical entity types (e.g., genes, chemicals and
diseases), it covers many new entity types related explicitly to the COVID-19
studies (e.g., coronaviruses, viral proteins, evolution, materials, substrates
and immune responses), which may benefit research on COVID-19 related virus,
spreading mechanisms, and potential vaccines. CORD-NER annotation is a
combination of four sources with different NER methods. The quality of CORD-NER
annotation surpasses SciSpacy (over 10% higher on the F1 score based on a
sample set of documents), a fully supervised BioNER tool. Moreover, CORD-NER
supports incrementally adding new documents as well as adding new entity types
when needed by adding dozens of seeds as the input examples. We will constantly
update CORD-NER based on the incremental updates of the CORD-19 corpus and the
improvement of our system.
