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One of the main computational and scientific challenges in the modern age is to extract useful information from unstructured texts. Topic models are one popular machine-learning approach which infers the latent topical structure of a collection of documents. Despite their success --- in particular of its most widely used variant called Latent Dirichlet Allocation (LDA) --- and numerous applications in sociology, history, and linguistics, topic models are known to suffer from severe conceptualdoi:10.1126/sciadv.aaq1360 pmid:30035215 pmcid:PMC6051742 fatcat:hb3mjafz7fdnvhw6whre6g2i6y