@InProceedings{adams-EtAl:2017:BioNLP17,
  author    = {Adams, Joel  and  Bedrick, Steven  and  Fergadiotis, Gerasimos  and  Gorman, Kyle  and  van Santen, Jan},
  title     = {Target word prediction and paraphasia classification in spoken discourse},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {1--8},
  abstract  = {We present a system for automatically detecting and classifying phonologically
	anomalous productions in the speech of individuals with aphasia.
	Working from transcribed discourse samples, our system identifies neologisms,
	and uses a combination of string alignment and language models to produce a
	lattice of plausible words that the speaker may have intended to produce.
	We then score this lattice according to various features, and attempt to
	determine whether the anomalous production represented a phonemic error or a
	genuine neologism.
	This approach has the potential to be expanded to consider other types of
	paraphasic errors, and could be applied to a wide variety of screening and
	therapeutic applications.},
  url       = {http://www.aclweb.org/anthology/W17-2301}
}