  The recent dramatic increase in online data availability has allowed
researchers to explore human culture with unprecedented detail, such as the
growth and diversification of language. In particular, it provides statistical
tools to explore whether word use is similar across languages, and if so,
whether these generic features appear at different scales of language
structure. Here we use the Google Books $N$-grams dataset to analyze the
temporal evolution of word usage in several languages. We apply measures
proposed recently to study rank dynamics, such as the diversity of $N$-grams in
a given rank, the probability that an $N$-gram changes rank between successive
time intervals, the rank entropy, and the rank complexity. Using different
methods, results show that there are generic properties for different languages
at different scales, such as a core of words necessary to minimally understand
a language. We also propose a null model to explore the relevance of linguistic
structure across multiple scales, concluding that $N$-gram statistics cannot be
reduced to word statistics. We expect our results to be useful in improving
text prediction algorithms, as well as in shedding light on the large-scale
features of language use, beyond linguistic and cultural differences across
human populations.
