Yulia Otmakhova, K. Verspoor, Timothy Baldwin, Simon Suster, Jey Han Lau
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Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration
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 pro-pose several ways to improve topic coherence and specificity.
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