(24-08-2025, 01:41 PM)quimqu wrote: You are not allowed to view links. Register or Login to view.If I follow your suggestion, I end up with topics 0 to N for language A and topics 0 to M for language B, but then I lose any potential connection between the two sets of topics. So, I feel this approach would fragment the analysis too much.
I'm not sure I agree. If the topics correspond to some actual underlying feature, I think it won't be hard to perform cross detection and identify the mappings between N topics for A and M topics for B. After all, you will have a model that detects topics, as far as I understand. If there is a clean match between A topics and B topics, this would boost the claim that the topics are not a random statistical split of data, but a grouping that is naturally present in the manuscript. On the other hand, if NMF topic modeling for A and NMF topic modeling for B produce incompatible topics with no connection, then what is the use of combining them into a single model? Obviously that would mean that either this separation into topics is spurious or topics exist separately in language A and language B.
Also, because you mentioned deciphering in another thread, I need to clarify that personally I don't find topic modeling of the Voynich Manuscript useful for deciphering attempts, so if you don't agree with my view above, it certainly doesn't make sense to adjust your approach. Unfortunately, I do not see any plausible path from modeling topics (or any other macro scale analysis) to actually deciphering the MS.