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Computer Science > Computation and Language

arXiv:2404.13439 (cs)
[Submitted on 20 Apr 2024]

Title:Fine-Grained Named Entities for Corona News

Authors:Sefika Efeoglu, Adrian Paschke
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Abstract: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 this https URL.
Comments: Published at SWAT4HCLS 2023: The 14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2404.13439 [cs.CL]
  (or arXiv:2404.13439v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2404.13439
Journal reference: CEUR-WS 2023

Submission history

From: Sefika Efeoglu [view email]
[v1] Sat, 20 Apr 2024 18:22:49 UTC (928 KB)
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