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.
|Title of host publication||Proceedings of the 14th International Conference on Natural Language Processing (ICON 2017)|
|Number of pages||10|
|Publication status||Published - 21 Dec 2017|
|Event||ICON 2017 - Kolkata, Kolkata, India|
Duration: 18 Dec 2017 → 21 Dec 2017
|Period||18/12/2017 → 21/12/2017|