  Differentiating intrinsic language words from transliterable words is a key
step aiding text processing tasks involving different natural languages. We
consider the problem of unsupervised separation of transliterable words from
native words for text in Malayalam language. Outlining a key observation on the
diversity of characters beyond the word stem, we develop an optimization method
to score words based on their nativeness. Our method relies on the usage of
probability distributions over character n-grams that are refined in step with
the nativeness scorings in an iterative optimization formulation. Using an
empirical evaluation, we illustrate that our method, DTIM, provides significant
improvements in nativeness scoring for Malayalam, establishing DTIM as the
preferred method for the task.
