TY - GEN
T1 - Target word prediction and paraphasia classification in spoken discourse
AU - Adams, Joel
AU - Bedrick, Steven
AU - Fergadiotis, Gerasimos
AU - Gorman, Kyle
AU - Van Santen, Jan
N1 - Funding Information:
We thank the BioNLP reviewers for their helpful comments and advice. This material is based upon work supported in part by the National Institute on Deafness and Other Communication Disorders of the National Institutes of Health under awards R01DC012033 and R03DC014556. The content is solely the responsibility of the authors and does not necessarily represent the official views of the granting agencies or any other individual.
Publisher Copyright:
© 2017 Association for Computational Linguistics
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85122919801
T3 - BioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop
SP - 1
EP - 8
BT - BioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 16th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2017
Y2 - 4 August 2017
ER -