In natural language processing (NLP), social media data has brought variation to the fore, spurring the development of new computational techniques for charac-terizing variation in the lexicon (Eisenstein et al., 2010), orthography (Eisenstein, 2015), and syntax (Blodgett et al., 2016).
aside from the focused task of spelling normalization (Sproat et al., 2001;Aw et al., 2006), there have been few attempts to make NLP systems more robust to language variation across speakers or writers.