That second part wasn’t super important though — this was more about learning and experimenting than trying to break new ground. Really appreciate the kind words, and hopefully it sparks someone to take it even further.

Clustering by sentence or page would be interesting too — I haven't gone that far yet, but it’d be fascinating to see if there’s consistency across visual/media sections. Appreciate the insight!

Appreciate the nudge — always fascinating to see where people take this kind of thinking.

My main goal was to learn and see if the manuscript behaved like a real language, not necessarily to translate it. Appreciate the link — I’ll check it out (once I get my German up to speed!).

The challenge (as I understand it) is that the vocabulary size is pretty massive — thousands of unique words — and the structure might not be 1:1 with how real language maps. Like, is a “word” in Voynich really a word? Or is it a chunk, or a stem with affixes, or something else entirely? That makes brute-forcing a direct mapping tricky.

That said… using cluster IDs instead of individual word (tokens) and scoring the outputs with something like a language model seems like a pretty compelling idea. I hadn’t thought of doing it that way. Definitely some room there for optimization or even evolutionary techniques. If nothing else, it could tell us something about how “language-like” the structure really is.

Might be worth exploring — thanks for tossing that out, hopefully someone with more awareness or knowledge in the space see's it!

Appreciate you calling that out — that’s a great push toward iteration.

And yes to the cross-cluster reference idea — I didn’t build a similarity matrix between clusters, but now that you’ve said it, it feels like an obvious next step to test how much signal is really being captured.

Might spin those up as a follow-up. Appreciate the thoughtful nudge.

I didn’t re-map anything back to glyphs in this project — everything’s built off those EVA transliterations as a starting point. So if "okeeodair" exists in the dataset, that’s because someone much smarter than me saw a sequence of glyphs and agreed to call it that.

The Voynich Manuscript is a 15th-century book written in an unknown script. No one’s been able to translate it, and many think it’s a hoax, a cipher, or a constructed language. I wasn’t trying to decode it — I just wanted to see: does it behave like a structured language?

I stripped a handful of common suffix-like endings (aiin, dy, etc.) to isolate what looked like root forms. I know that’s a strong assumption — I call it out directly in the repo — but it helped clarify the clustering. From there, I used SBERT embeddings and KMeans to group similar roots, inferred POS-like roles based on position and frequency, and built a Markov transition matrix to visualize cluster-to-cluster flow.

It’s not translation. It’s not decryption. It’s structural modeling — and it revealed some surprisingly consistent syntax across the manuscript, especially when broken out by section (Botanical, Biological, etc.).

GitHub repo: https://github.com/brianmg/voynich-nlp-analysis Write-up: https://brig90.substack.com/p/modeling-the-voynich-manuscrip...

I’m new to the NLP space, so I’m sure there are things I got wrong — but I’d love feedback from people who’ve worked with structured language modeling or weird edge cases like this.

Comments URL: https://news.ycombinator.com/item?id=44022353

Points: 381

# Comments: 132