A Novel Dataset Towards Extracting Virus-Host Interactions
Rasha Alshawi Atriya Sen Nathan S. Upham Beckett Sterner
We describe a novel dataset for the automated recognition of named taxonomic
and other entities relevant to the association of viruses with their hosts. We
further describe some initial results using pre-trained models on the
named-entity recognition (NER) task on this novel dataset. We propose that our
dataset of manually annotated abstracts now offers a Gold Standard Corpus for
training future NER models in the automated extraction of host-pathogen
detection methods from scientific publications, and further explain how our
work makes first steps towards predicting the important human health-related
concept of viral spillover risk automatically from the scientific literature.