Automated Topic Analysis of the Voynich Manuscript

[Jorge_Stolfi]

Then I turn each paragraph into a bag-of-words

Ah, ok - so your unit is the parag, not the page.

The model learns two things: a set of word distributions (the topics themselves) and, for each paragraph, a vector of proportions telling how those topics are mixed. You choose K, the number of topics; too small and themes are merged, too large and they fragment.

So you are trying several K-clustering algorithms on the same set of data points, where each point is a bag-of-words -- essentially an N-vector where N is the number of distinct word types.  Where the algorithms do not assign each parag to a single cluster (topic), but gives it a "belonging" or "mixing" score for each cluster.  Is that correct?

Can we interpret those scores as Bayesian probabilities of the parag belonging to each topic?

All the best, --jorge