  Hidden structural patterns in written texts have been subject of considerable
research in the last decades. In particular, mapping a text into a time series
of sentence lengths is a natural way to investigate text structure. Typically,
sentence length has been quantified by using measures based on the number of
words and the number of characters, but other variations are possible. To
quantify the robustness of different sentence length measures, we analyzed a
database containing about five hundred books in English. For each book, we
extracted six distinct measures of sentence length, including number of words
and number of characters (taking into account lemmatization and stop words
removal). We compared these six measures for each book by using i) Pearson's
coefficient to investigate linear correlations; ii) Kolmogorov--Smirnov test to
compare distributions; and iii) detrended fluctuation analysis (DFA) to
quantify auto-correlations. We have found that all six measures exhibit very
similar behavior, suggesting that sentence length is a robust measure related
to text structure.
