A Comprehensive Dictionary and Term Variation Analysis for COVID-19 and SARS-CoV-2
This work addresses a domain-specific issue for researchers and text miners dealing with COVID-19 literature, but it is incremental as it builds on existing terminological resources.
The authors tackled the problem of high term variation for SARS-CoV-2 and COVID-19 in scientific literature, which hinders entity identification, by creating a comprehensive dictionary using a rule-based approach to generate and locate term variants, resulting in a substantial number of additional terms compared to existing resources.
The number of unique terms in the scientific literature used to refer to either SARS-CoV-2 or COVID-19 is remarkably large and has continued to increase rapidly despite well-established standardized terms. This high degree of term variation makes high recall identification of these important entities difficult. In this manuscript we present an extensive dictionary of terms used in the literature to refer to SARS-CoV-2 and COVID-19. We use a rule-based approach to iteratively generate new term variants, then locate these variants in a large text corpus. We compare our dictionary to an extensive collection of terminological resources, demonstrating that our resource provides a substantial number of additional terms. We use our dictionary to analyze the usage of SARS-CoV-2 and COVID-19 terms over time and show that the number of unique terms continues to grow rapidly. Our dictionary is freely available at https://github.com/ncbi-nlp/CovidTermVar.