Some years later, many researchers incorporated machine learning algorithms to their systems, but there was still a strong dependency on external resources and domain-specific features and rules (Tjong Kim Sang and De Meulder, 2003).
the most important differences of our approach and previous works are i) the use of phonetics and phonology (articulatory) features at the character level to model SM noise, ii) consistent BLStMs for character and word levels, iii) the segmentation and categorization tasks, iv) a multitask neural network that transfers the learning without using lexicons or gazetteers, and v) weighted classes to handle the inherent skewness of the datasets.