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 - Funding Information:
This material is based upon work supported by the National Institute on Deafness and Other Communication Disorders of the National Institutes of Health under awards R01DC007129 and R01DC012033, and by Autism Speaks under Innovative Technology for Autism Grant 2407. 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.
Funding Information:
All experiments were conducted using OpenFst (Allauzen et al., 2007), OpenGrm-Thrax (Roark et al., 2012), and Python 3.4. A demonstration version of the system can be viewed at the following URL: http://sonny.cslu.ohsu.edu:8080. Thanks to the other members of the CSLU autism research group, and to Emily Prud?hommeaux and Mabel Rice. This material is based upon work supported by the National Institute on Deafness and Other Communication Disorders of the National Institutes of Health under awards R01DC007129 and R01DC012033, and by Autism Speaks under Innovative Technology for Autism Grant 2407. 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:
© 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.
UR - http://www.scopus.com/inward/record.url?scp=84966367655&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84966367655&partnerID=8YFLogxK
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 -