Normalizing Non-canonical Turkish texts using machine translation approaches

With the growth of the social web, usergenerated text data has reached unprecedented sizes. Non-canonical text normalization provides a way to exploit this as a practical source of training data for language processing systems. The state of the art in Turkish text normalization is composed of a tokenlevel pipeline of modules, heavily dependent on external linguistic resources and manuallydefined rules. Instead, we propose a fullyautomated, context-aware machine translation approach with fewer stages of processing. Experiments with various implementations of our approach show that we are able to surpass the current best-performing system by a large margin.