TY - GEN
T1 - Automated morphological analysis of clinical language samples
AU - Gorman, Kyle
AU - Bedrick, Steven
AU - Kiss, Géza
AU - Morley, Eric
AU - Ingham, Rosemary
AU - Mohammad, Metrah
AU - Papadakis, Katina
AU - van Santen, Jan P.H.
N1 - Publisher Copyright:
© 2015 Association for Computational Linguistics
PY - 2015
Y1 - 2015
N2 - Quantitative analysis of clinical language samples is a powerful tool for assessing and screening developmental language impairments, but requires extensive manual transcription, annotation, and calculation, resulting in error-prone results and clinical underutilization. We describe a system that performs automated morphological analysis needed to calculate statistics such as the mean length of utterance in morphemes (MLUM), so that these statistics can be computed directly from orthographic transcripts. Estimates of MLUM computed by this system are closely comparable to those produced by manual annotation. Our system can be used in conjunction with other automated annotation techniques, such as maze detection. This work represents an important first step towards increased automation of language sample analysis, and towards attendant benefits of automation, including clinical greater utilization and reduced variability in care delivery.
AB - Quantitative analysis of clinical language samples is a powerful tool for assessing and screening developmental language impairments, but requires extensive manual transcription, annotation, and calculation, resulting in error-prone results and clinical underutilization. We describe a system that performs automated morphological analysis needed to calculate statistics such as the mean length of utterance in morphemes (MLUM), so that these statistics can be computed directly from orthographic transcripts. Estimates of MLUM computed by this system are closely comparable to those produced by manual annotation. Our system can be used in conjunction with other automated annotation techniques, such as maze detection. This work represents an important first step towards increased automation of language sample analysis, and towards attendant benefits of automation, including clinical greater utilization and reduced variability in care delivery.
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M3 - Conference contribution
AN - SCOPUS:84966367655
T3 - 2nd Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, CLPsych 2015 - Proceedings of the Workshop
SP - 108
EP - 116
BT - 2nd Computational Linguistics and Clinical Psychology
PB - Association for Computational Linguistics (ACL)
T2 - 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, CLPsych 2015
Y2 - 5 June 2015
ER -