Manual versus automated narrative analysis of agrammatic production patterns

The northwestern narrative language analysis and computerized language analysis

Chien Ju Hsu*, Cynthia K Thompson

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)373-385
Number of pages13
JournalJournal of Speech, Language, and Hearing Research
Volume61
Issue number2
DOIs
StatePublished - Feb 1 2018

Fingerprint

pattern of production
language analysis
Language
narrative
coding
speech disorder
Aphasia
Narrative Language
Narrative Analysis
Broca Aphasia
Aptitude
deficit
Healthy Volunteers

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Speech and Hearing

Cite this

@article{b8095d4aa7dc4232a0837de33e031e52,
title = "Manual versus automated narrative analysis of agrammatic production patterns: The northwestern narrative language analysis and computerized language analysis",
abstract = "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.",
author = "Hsu, {Chien Ju} and Thompson, {Cynthia K}",
year = "2018",
month = "2",
day = "1",
doi = "10.1044/2017_JSLHR-L-17-0185",
language = "English (US)",
volume = "61",
pages = "373--385",
journal = "Journal of Speech, Language, and Hearing Research",
issn = "1092-4388",
publisher = "American Speech-Language-Hearing Association (ASHA)",
number = "2",

}

TY - JOUR

T1 - Manual versus automated narrative analysis of agrammatic production patterns

T2 - The northwestern narrative language analysis and computerized language analysis

AU - Hsu, Chien Ju

AU - Thompson, Cynthia K

PY - 2018/2/1

Y1 - 2018/2/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85042168319&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85042168319&partnerID=8YFLogxK

U2 - 10.1044/2017_JSLHR-L-17-0185

DO - 10.1044/2017_JSLHR-L-17-0185

M3 - Article

VL - 61

SP - 373

EP - 385

JO - Journal of Speech, Language, and Hearing Research

JF - Journal of Speech, Language, and Hearing Research

SN - 1092-4388

IS - 2

ER -