Topic Modeling in the Voynich Manuscript
Rachel Sterneck, Annie Polish, Claire Bowern
July 2021

This article presents the results of investigations using topic modeling of the Voynich Manuscript (Beinecke Library MS408). Topic modeling is a set of computational methods which are used to identify clusters of subjects within text. We use latent dirichlet allocation, latent semantic analysis, and nonnegative matrix factorization to cluster Voynich pages into `topics'. We then compare the topics derived from the computational models to clusters derived from the Voynich illustrations and from paleographic analysis. We find that computationally derived clusters match closely to a conjunction of scribe and subject matter (as per the illustrations), providing further evidence that the Voynich Manuscript contains meaningful text.
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Reference: lingbuzz/006068
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keywords: tf-idf, topic modeling, nmf, morphology, syntax, phonology
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