  This paper experiments with frequency-based corpus similarity measures across
39 languages using a register prediction task. The goal is to quantify (i) the
distance between different corpora from the same language and (ii) the
homogeneity of individual corpora. Both of these goals are essential for
measuring how well corpus-based linguistic analysis generalizes from one
dataset to another. The problem is that previous work has focused on
Indo-European languages, raising the question of whether these measures are
able to provide robust generalizations across diverse languages. This paper
uses a register prediction task to evaluate competing measures across 39
languages: how well are they able to distinguish between corpora representing
different contexts of production? Each experiment compares three corpora from a
single language, with the same three digital registers shared across all
languages: social media, web pages, and Wikipedia. Results show that measures
of corpus similarity retain their validity across different language families,
writing systems, and types of morphology. Further, the measures remain robust
when evaluated on out-of-domain corpora, when applied to low-resource
languages, and when applied to different sets of registers. These findings are
significant given our need to make generalizations across the rapidly
increasing number of corpora available for analysis.
