  The average uncertainty associated with words is an information-theoretic
concept at the heart of quantitative and computational linguistics. The entropy
has been established as a measure of this average uncertainty - also called
average information content. We here use parallel texts of 21 languages to
establish the number of tokens at which word entropies converge to stable
values. These convergence points are then used to select texts from a massively
parallel corpus, and to estimate word entropies across more than 1000
languages. Our results help to establish quantitative language comparisons, to
understand the performance of multilingual translation systems, and to
normalize semantic similarity measures.
