构建越南语新冠嵌套实体识别数据集，助力疫情追踪自动化
Nested Named-Entity Recognition on Vietnamese COVID-19: Dataset and Experiments
- 设计针对越南语新冠文本的嵌套实体识别任务
- 创建首个标注完整的越南语新冠嵌套实体数据集
- 为疫情溯源系统提供可直接使用的中文本处理工具
新冠疫情全球肆虐，各国防控成效不一。越南通过人工追踪、定位和隔离接触者，有效控制疫情传播，但耗时费力。本文开展越南语命名实体识别（NER）研究，旨在辅助疫情防控。我们构建了首个支持嵌套实体识别的越南语新冠领域数据集，定义了新的实体类型以满足系统需求，并完成相关实验。
原文摘要 · Abstract (English)
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.
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