Denominative variation in the COVID-19 Open Research Dataset corpus

Abstract

Abstract Since 2020, we have witnessed the emergence of new concepts and terms due to the pandemic outbreak. Some of them have even become obsolete in a short period of time whereas others are still misused despite standardization efforts. In this paper we study explicit denominative variation in the COVID-19 corpus, which consists of scientific articles released as part of the COVID-19 Open Research Dataset and is publicly available in Sketch Engine. First of all, variants for severe acute respiratory syndrome coronavirus 2 and coronavirus disease 2019 were extracted by means of knowledge patterns (e.g., also known as ). The productiveness of knowledge patterns was analyzed and a set of 1,684 explicit variation excerpts were collected and manually annotated. A total of 371 variants were retrieved and organized in two polydenominative clusters (i.e., 177 for COVID-19 and 193 for SARS-CoV-2), which were then formally and semantically characterized by comparison with the established designations. Finally, possible causes underlying denominative variation are explored.