Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique
Ibai Guillén-Pacho,
Carlos Badenes-Olmedo,
Oscar Corcho
Abstract:Purpose: The work presented in this article focusses on improving the interpretability of probabilistic topic models created from a large collection of scientific documents that evolve over time.
Methods: Several time-dependent approaches based on topic models were compared to analyse the annual evolution of latent concepts in the CORD-19 corpus: Dynamic Topic Model, Dynamic Embedded Topic Model, and BERTopic. Then COVID-19 period (December 2019 - present) has been analysed in greater depth, month by month, t… Show more
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