Fine-Grained Named Entities for Corona News

TL;DR

This paper presents a data annotation pipeline and trained named entity recognition models for extracting structured information from corona news articles, aiding in efficient analysis of unstructured pandemic-related texts.

Contribution

It introduces a novel annotation pipeline and domain-specific NER models tailored for corona news articles, enhancing information extraction in this domain.

Findings

High accuracy of NER models on test data

Effective extraction of corona-related entities

Open-source code available for replication

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 https://github.com/sefeoglu/coronanews-ner.git.