Manual Versus Automated Narrative Analysis of Agrammatic Production Patterns: The Northwestern Narrative Language Analysis and Computerized Language Analysis Purpose The purpose of this study is to compare the outcomes of the manually coded Northwestern Narrative Language Analysis (NNLA) system, which was developed for characterizing agrammatic production patterns, and the automated Computerized Language Analysis (CLAN) system, which has recently been adopted to analyze speech samples of individuals with aphasia ... Research Article
Research Article  |   February 15, 2018
Manual Versus Automated Narrative Analysis of Agrammatic Production Patterns: The Northwestern Narrative Language Analysis and Computerized Language Analysis
 
Author Affiliations & Notes
  • Chien-Ju Hsu
    Neurolinguistics and Aphasia Research Laboratory, Center for the Neurobiology of Language Recovery, The Roxelyn & Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL
  • Cynthia K. Thompson
    Neurolinguistics and Aphasia Research Laboratory, Center for the Neurobiology of Language Recovery, The Roxelyn & Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL
    Cognitive Neurology and Alzheimer's Disease Center, Northwestern Feinberg School of Medicine, Chicago, IL
    The Ken & Ruth Davee Department of Neurology, Northwestern Feinberg School of Medicine, Chicago, IL
  • Disclosure: The authors have declared that no competing interests existed at the time of publication.
    Disclosure: The authors have declared that no competing interests existed at the time of publication. ×
  • Correspondence to Chien-Ju Hsu: chienjuhsu@u.northwestern.edu
  • Editor-in-Chief: Sean Redmond
    Editor-in-Chief: Sean Redmond×
  • Editor: Carl Coelho
    Editor: Carl Coelho×
Article Information
Research Issues, Methods & Evidence-Based Practice / Language Disorders / Speech, Voice & Prosody / Language / Research Articles
Research Article   |   February 15, 2018
Manual Versus Automated Narrative Analysis of Agrammatic Production Patterns: The Northwestern Narrative Language Analysis and Computerized Language Analysis
Journal of Speech, Language, and Hearing Research, February 2018, Vol. 61, 373-385. doi:10.1044/2017_JSLHR-L-17-0185
History: Received May 17, 2017 , Revised September 25, 2017 , Accepted October 11, 2017
 
Journal of Speech, Language, and Hearing Research, February 2018, Vol. 61, 373-385. doi:10.1044/2017_JSLHR-L-17-0185
History: Received May 17, 2017; Revised September 25, 2017; Accepted October 11, 2017

Purpose The purpose of this study is to compare the outcomes of the manually coded Northwestern Narrative Language Analysis (NNLA) system, which was developed for characterizing agrammatic production patterns, and the automated Computerized Language Analysis (CLAN) system, which has recently been adopted to analyze speech samples of individuals with aphasia (a) for reliability purposes to ascertain whether they yield similar results and (b) to evaluate CLAN for its ability to automatically identify language variables important for detailing agrammatic production patterns.

Method The same set of Cinderella narrative samples from 8 participants with a clinical diagnosis of agrammatic aphasia and 10 cognitively healthy control participants were transcribed and coded using NNLA and CLAN. Both coding systems were utilized to quantify and characterize speech production patterns across several microsyntactic levels: utterance, sentence, lexical, morphological, and verb argument structure levels. Agreement between the 2 coding systems was computed for variables coded by both.

Results Comparison of the 2 systems revealed high agreement for most, but not all, lexical-level and morphological-level variables. However, NNLA elucidated utterance-level, sentence-level, and verb argument structure–level impairments, important for assessment and treatment of agrammatism, which are not automatically coded by CLAN.

Conclusions CLAN automatically and reliably codes most lexical and morphological variables but does not automatically quantify variables important for detailing production deficits in agrammatic aphasia, although conventions for manually coding some of these variables in Codes for the Human Analysis of Transcripts are possible. Suggestions for combining automated programs and manual coding to capture these variables or revising CLAN to automate coding of these variables are discussed.

Acknowledgments
The authors would like to thank Jennifer E. Mack and Jiyeon Lee for reliability coding and the researchers from the AphasiaBank project at Carnegie Mellon University for their training and for preparing the agrammatic samples.
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