Nested Named-Entity Recognition on Vietnamese COVID-19: Dataset and Experiments
This work addresses the labor-intensive manual process of contact tracing for COVID-19 in Vietnam, but it is incremental as it applies existing NER methods to a new domain-specific dataset.
The authors tackled the problem of automating COVID-19 contact tracing in Vietnam by developing a named-entity recognition system, resulting in a manually annotated dataset for nested entities in Vietnamese.
The COVID-19 pandemic caused great losses worldwide, efforts are taken place to prevent but many countries have failed. In Vietnam, the traceability, localization, and quarantine of people who contact with patients contribute to effective disease prevention. However, this is done by hand, and take a lot of work. In this research, we describe a named-entity recognition (NER) study that assists in the prevention of COVID-19 pandemic in Vietnam. We also present our manually annotated COVID-19 dataset with nested named entity recognition task for Vietnamese which be defined new entity types using for our system.