Multi-label classification of COVID-19-related articles with an autoML approach
Ilija Tavchioski, Boshko Koloski, Blaž Škrlj, Senja Pollak · Zenodo (CERN European Organization for Nuclear Research) · 2022
The rapid growth of literature related to the COVID-19 pandemic results in a multitude of articles which cannot be manually labeled due to the lack of human resources, In this work we present a solution to the shared task titled LitCovid track Multi-label topic classification for COVID-19 literature annotation. Our proposed solution constructs classifiers for each class by using an autoML system for text named autoBOT. Albeit the proposed system performed sub-optimally in terms of recall, it offered better-than-baseline (macro) precision, indication that automated representation learning is a promising approach to multilabel classification of COVID-19-related texts.