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Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique

2023-05-12

Abstract excerpt

<h4>Purpose: </h4> 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. <h4>Methods: </h4>: 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 Mod...

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Literature Corpus work
d17ddacd-6d63-5426-b38a-8ab34293c87e
DOI
10.21203/rs.3.rs-2872880/v1
Open publication

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Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling TechniqueDOI 10.21203/rs.3.rs-2872880/v1
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