  Quite often, words from one language are adopted within a different language
without translation; these words appear in transliterated form in text written
in the latter language. This phenomenon is particularly widespread within
Indian languages where many words are loaned from English. In this paper, we
address the task of identifying loanwords automatically and in an unsupervised
manner, from large datasets of words from agglutinative Dravidian languages. We
target two specific languages from the Dravidian family, viz., Malayalam and
Telugu. Based on familiarity with the languages, we outline an observation that
native words in both these languages tend to be characterized by a much more
versatile stem - stem being a shorthand to denote the subword sequence formed
by the first few characters of the word - than words that are loaned from other
languages. We harness this observation to build an objective function and an
iterative optimization formulation to optimize for it, yielding a scoring of
each word's nativeness in the process. Through an extensive empirical analysis
over real-world datasets from both Malayalam and Telugu, we illustrate the
effectiveness of our method in quantifying nativeness effectively over
available baselines for the task.
