The rise of social media has brought computational linguistics in ever-closer contact with bad language: text that defies our expectations about vocabulary, spelling, and syntax. This paper surveys the landscape of bad language, and offers a critical review of the NLP community's response, which has largely followed two paths: normalization and domain adaptation. Each approach is evaluated in the context of theoretical and empirical work on computer-mediated communication. In addition, the paper presents a quantitative analysis of the lexical diversity of social media text, and its relationship to other corpora.
Eisenstein REF outlines the challenges posed by the bad language that characterizes the Internet, and surveys two of the most popular directions in which the NLP community has tackled this issue: normalization and domain adaptation.
What to do about bad language on the internet
NAACL
Computer Science