Nested Named-Entity Recognition on Vietnamese COVID-19: Dataset and Experiments
Computation and Language
2025-06-17 v2 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2504.21016,
title = {Nested Named-Entity Recognition on Vietnamese COVID-19: Dataset and Experiments},
author = {Ngoc C. Lê and Hai-Chung Nguyen-Phung and Thu-Huong Pham Thi and Hue Vu and Phuong-Thao Nguyen Thi and Thu-Thuy Tran and Hong-Nhung Le Thi and Thuy-Duong Nguyen-Thi and Thanh-Huy Nguyen},
journal= {arXiv preprint arXiv:2504.21016},
year = {2025}
}
Comments
8 pages. AI4SG-21 The 3rd Workshop on Artificial Intelligence for Social Good at IJCAI 2021