[split] Word length autocorrelation

[nablator]

You’re right, the lag-1 autocorrelation I used is just the standard Pearson correlation between word length and the next word’s length.

It's interesting that Gaskell and Bowern found the opposite for natural languages

The way they calculated is (I think) simply a count of positive and negative autocorrelations to see which one is dominant.

This correlation is not Pearson correlation:
A positive autocorrelation is short-short or long-long
A negative autocorrelation is short-long or long-short

Every word is either short or long:
long if word length > mean word length
short if word length <= mean word length

I did something similar with: (number of positive autocorrelations for all word bigrams - number of negative autocorrelations for all word bigrams) / number of all word bigrams

Testing this on a few texts I found an exception to the rule of dominant negative autocorrelation in Latin poetry: Marbodus Redonensis Carmina varia (available on Corpus Corporum) is slightly positive (0.009), but Voynichese (EVA) is more positive (0.16). Still positive when all words (not only within each line) are scrambled (0.06). Same results with median word length instead of mean.