Non-standard linguistic features have been analysed both qualitatively and quantitatively (Eisenstein, 2013;Hu et al., 2013;Baldwin et al., 2013) and they have been taken into account in automatic text processing applications, which either strive to normalise non-standard features before submitting them to standard text processing tools (Han et al., 2012), adapt standard processing tools to work on non-standard data (Gimpel et al., 2011) or, in task-oriented applications, use a series of simple pre-processing steps to tackle the most frequent UGC-specific phenomena (Foster et al., 2011).
the learning of the technical and linguistic dimensions seem to be equally hard when there are up to 100 instances available for learning, the technical dimension taking off after that.