[split] Word length autocorrelation

[quimqu]

I think we mean the same but use different words.

By bigram I mean word bigram, not character bigram. The autocorrelation (positive or negative) is a property of a word bigram. So I am counting how many of these bigrams (sequences of two words on the same line, no word bigram across lines) are positively autocorrelated = p and how many are negatively correlated = n. The result is (p-n)/(p+n) = 0.169

Hi nablator,
Thanks: using your method (p−n)/(p+n)(p-n)/(p+n)(p−n)/(p+n) I also get about 0.19, close to your result.
The difference is that I usually work with Pearson autocorrelation line by line, which gives mostly negative values (like natural languages). But when computed on the whole corpus, Pearson turns positive, in line with Gaskell & Bowern (around 0.16).

So the surprising part is really this:

• Line level: negative, natural-like.
  
• Corpus level: positive, gibberish-like.
  

That contrast seems to be a key feature of Voynichese.That contrast seems to be a key feature of Voynichese.

One complication for this sort of analysis is that in the available transcriptions there are many uncertain spaces (EVA comma) that may or may not be word breaks.  And there are many spaces in the text that are wider than normal inter-glyph spaces but narrower than normal inter-word spaces, which transcribers may have either entered as word breaks (EVA period) or just ignored.  

Many of these dubious word breaks are after a short prefix like y or ol or before a short suffix like dy or ar.  If those dubious spaces were treated as word breaks, the length correlation would probably drop, possibly even becoming negative.

All the best, --jorge

You’re right, there are definitely uncertainties in the transcriptions (commas, periods, ambiguous spaces). That could affect the exact values of word-length correlation.

But since I’m calculating autocorrelation line by line, those local errors should average out and not flip the overall sign. In other words, they might shift the correlation slightly, but they wouldn’t normally turn a negative profile into a positive one.

That’s why the contrast between line-level (negative) and whole-corpus (positive) looks "robust", even allowing for those transcription issues.