Syntactic Complexity of Learning Content in Italian for COVID-19 Frontline Responders: A Study on WHO’s Emergency Learning Platform

Abstract

The goal of this paper is to offer a model to quantify the level of complexity of the linguistic content of a corpus in Italian extracted from OpenWHO, WHO’s health emergency learning platform (Rohloff et al. 2018; Zhao et al. 2019). The nature of the computational ranking costs of a typology of relativization strategies is investigated. To reach this goal, the results of the corpus are compared with other three syntactic annotated corpora from Italian belonging to different genres (news, social media, encyclopedic entries, legal). The results show that online learning contents in public health reduce complex structures in syntactic terms. The case study presented here provides a methodology to quantify syntactic and computational complexity in corpus studies.