We propose that what is traditionally categorized as noise (i.e., misspellings, inconsistent orthography, emerging abbreviations, and slang) should be modeled as is since it is an inherent characteristic of SM text.
the proposed models attempt to address i) misspellings using subword level representations, ii) grammatical mistakes with SM-oriented Part-of-Speech tags (Owoputi et al., 2013), iii) sounddriven text with phonetic and phonological features , and iv) the intrinsic skewness of NER datasets by applying class weights.