# Fine-Grained Named Entities for Corona News

arXiv: https://arxiv.org/abs/2404.13439 · HTML: https://mlsanity.com/paper/2404.13439/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 20 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [COVID-19 Named Entity Recognition for Vietnamese](https://mlsanity.com/paper/2104.03879/) (2021)
- [Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision](https://mlsanity.com/paper/2003.12218/) (2020)
- [Automatic Textual Evidence Mining in COVID-19 Literature](https://mlsanity.com/paper/2004.12563/) (2020)

## Abstract (arXiv)

Information resources such as newspapers have produced unstructured text data in various languages related to the corona outbreak since December 2019. Analyzing these unstructured texts is time-consuming without representing them in a structured format; therefore, representing them in a structured format is crucial. An information extraction pipeline with essential tasks -- named entity tagging and relation extraction -- to accomplish this goal might be applied to these texts. This study proposes a data annotation pipeline to generate training data from corona news articles, including generic and domain-specific entities. Named entity recognition models are trained on this annotated corpus and then evaluated on test sentences manually annotated by domain experts evaluating the performance of a trained model. The code base and demonstration are available at https://github.com/sefeoglu/coronanews-ner.git.