Fine-Grained Named Entities for Corona News
<|reference_start|>Fine-Grained Named Entities for Corona News: 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.<|reference_end|>
@article{efeoglu2024fine-grained,
    title={Fine-Grained Named Entities for Corona News},
    author={Sefika Efeoglu and Adrian Paschke},
    journal={CEUR-WS 2023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13439},
    primaryClass={cs.CL}
}