@inproceedings{adams-etal-2017-target,
    title = "Target word prediction and paraphasia classification in spoken discourse",
    author = "Adams, Joel  and
      Bedrick, Steven  and
      Fergadiotis, Gerasimos  and
      Gorman, Kyle  and
      van Santen, Jan",
    editor = "Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-2301",
    doi = "10.18653/v1/W17-2301",
    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.",
}