Improved POS tagging for spontaneous, clinical speech using data
  augmentation
Seth Kulick Neville Ryant David J. Irwin Naomi Nevler Sunghye Cho
This paper addresses the problem of improving POS tagging of transcripts of
speech from clinical populations. In contrast to prior work on parsing and POS
tagging of transcribed speech, we do not make use of an in domain treebank for
training. Instead, we train on an out of domain treebank of newswire using data
augmentation techniques to make these structures resemble natural, spontaneous
speech. We trained a parser with and without the augmented data and tested its
performance using manually validated POS tags in clinical speech produced by
patients with various types of neurodegenerative conditions.