11-11-2025, 09:27 PM
Hello,
I did some experiments also with silabes with the Voynich EVA last week. I broke each word into smaller, frequent character sequences (I call them segments). Then I counted every possible sequence of 2grams - 5grams that appear often in the corpus, to get the basic "building blocks". I used then a simple algorithm to find the best way to split each word into those pieces (it tries every possible way to split a word into smaller pieces and then chooses the split that gives the highest total score; each possible piece has then a score based on how often it appears in the whole text.). After segmenting all words, I counted how often each piece appeared at the beginning, middle or end of words, and finally I grouped them into prefixes, stems, sufixes or others. This are the top results by category:
You can see that there are way less sufixes than the rest. In total I got:
Prefixes: 69
Stems: 38
Sufixes: 22
Others: 804
There are curiosiies like "qo", "qok" and "qoke" that appear as different prefixes. They appear as prefixes because the algorithm looks at frequency and position, not meaning. Since “qo”, “qok”, and “qoke” often occur at the beginnings of different words, all of them score high as starting segments. The model treats each frequent beginning separately instead of assuming one is part of another.
I did some experiments also with silabes with the Voynich EVA last week. I broke each word into smaller, frequent character sequences (I call them segments). Then I counted every possible sequence of 2grams - 5grams that appear often in the corpus, to get the basic "building blocks". I used then a simple algorithm to find the best way to split each word into those pieces (it tries every possible way to split a word into smaller pieces and then chooses the split that gives the highest total score; each possible piece has then a score based on how often it appears in the whole text.). After segmenting all words, I counted how often each piece appeared at the beginning, middle or end of words, and finally I grouped them into prefixes, stems, sufixes or others. This are the top results by category:
You can see that there are way less sufixes than the rest. In total I got:
Prefixes: 69
Stems: 38
Sufixes: 22
Others: 804
There are curiosiies like "qo", "qok" and "qoke" that appear as different prefixes. They appear as prefixes because the algorithm looks at frequency and position, not meaning. Since “qo”, “qok”, and “qoke” often occur at the beginnings of different words, all of them score high as starting segments. The model treats each frequent beginning separately instead of assuming one is part of another.
