Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision
This provides a comprehensive NER resource for COVID-19 research, benefiting studies on the virus, spreading mechanisms, and vaccines, but it is incremental as it builds on existing methods and datasets.
The authors tackled the problem of named entity recognition on the COVID-19 research corpus by creating the CORD-NER dataset, which covers 75 fine-grained entity types and achieves over 10% higher F1 score than SciSpacy on a sample set.
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.