Topic Modeling in the Voynich Manuscript
This work addresses the long-standing mystery of the Voynich Manuscript's content for historians and linguists, but it is incremental as it builds on existing computational methods without major breakthroughs.
The researchers applied topic modeling methods to the Voynich Manuscript to cluster its pages into topics and found that these computational clusters closely matched clusters based on illustrations and scribe analysis, providing evidence that the manuscript contains meaningful text.
This article presents the results of investigations using topic modeling of the Voynich Manuscript (Beinecke 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.