Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration

Yulia Otmakhova, Karin Verspoor, Timothy Baldwin, Simon Suster

Keywords: topic modelling, lda, bio-NLP, covid19

Abstract: Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose. In this study we compare traditional topic models based on word tokens with topic models based on medical concepts, and propose several ways to improve topic coherence and specificity.