Could Linguistic Complexity Be Automatically Evaluated? : A Multilingual Study on WHO's Emergency Learning Platform

Abstract

The ability of assessing any type of linguistic complexity of any given contents could potentially improve knowledge reproduction, especially tacit knowledge which can be expensive during a pandemic. In this paper, we develop a simple and crosslinguistic model of complexity which considers formal accounts on the study of linguistic systems, but can be easily implemented by non-linguists' groups, e.g., communication experts and policymakers. To test our model, we conduct a study on a corpus extracted from the World Health Organization (WHO)'s emergency learning platform in 6 languages. Data extracted from open-access encyclopaedic entries act as control groups. The results show that the measurements adopted signal a trend for a minimization of complexity and can be exploited as features for (automatic) text classification.