A Rule-based Kurdish Text Transliteration System
This work addresses text mining challenges for Kurdish language processing, but it is incremental as it applies existing rule-based methods to a new language domain.
The authors tackled the problem of transliterating between two orthographies in Sorani Kurdish using a rule-based system, achieving 82.79% overall precision and over 99% accuracy in detecting double-usage characters.
In this article, we present a rule-based approach for transliterating two mostly used orthographies in Sorani Kurdish. Our work consists of detecting a character in a word by removing the possible ambiguities and mapping it into the target orthography. We describe different challenges in Kurdish text mining and propose novel ideas concerning the transliteration task for Sorani Kurdish. Our transliteration system, named Wergor, achieves 82.79% overall precision and more than 99% in detecting the double-usage characters. We also present a manually transliterated corpus for Kurdish.