  This paper investigates data-driven segmentation using Re-Pair or Byte Pair
Encoding-techniques. In contrast to previous work which has primarily been
focused on subword units for machine translation, we are interested in the
general properties of such segments above the word level. We call these
segments r-grams, and discuss their properties and the effect they have on the
token frequency distribution. The proposed approach is evaluated by
demonstrating its viability in embedding techniques, both in monolingual and
multilingual test settings. We also provide a number of qualitative examples of
the proposed methodology, demonstrating its viability as a language-invariant
segmentation procedure.

Computation and Language;Machine Learning