Manual Versus Automated Narrative Analysis of Agrammatic Production Patterns: The Northwestern Narrative Language Analysis and Computerized Language Analysis
Manual Versus Automated Narrative Analysis of Agrammatic Production Patterns: The Northwestern Narrative Language Analysis and Computerized Language Analysis
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DOI：
10.1044/2017_jslhr-l-17-0185
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发表时间：
2018-02-01
影响因子：
2.6
通讯作者：
Thompson, Cynthia K.
中科院分区：
文献类型：
--
作者：
Hsu, Chien-Ju;Thompson, Cynthia K.
目的：本研究的目的是比较人工编码的西北叙事语言分析（NNLA）系统，这是为表征语法生产模式，和自动化的计算机语言分析（CLAN）系统的结果，该方法最近被用于分析失语症患者的语音样本（a）用于可靠性目的，以确定它们是否产生类似的结果，以及（B）方法：采用NNLA和CLAN对来自8名临床诊断为无语法性失语症的参与者和10名认知健康的对照参与者的同一组Cinderella叙述样本进行转录和编码。这两个编码系统被用来量化和表征语音生产模式在几个微句法层次：话语，句子，词汇，形态，和动词的论点结构水平。两个编码系统之间的协议计算的变量codedby both.Results：2个系统的比较显示，大多数，但不是所有的，词汇水平和形态水平的变量的高协议。然而，NNLA阐明了话语水平，话语水平和动词论元结构水平的损伤，这对于语法缺失的评估和治疗非常重要，CLAN不会自动编码。CLAN自动和可靠地编码大多数词汇和形态变量，但不能自动量化变量，这些变量对详细描述语法失用性失语症的产生缺陷很重要，尽管在人工分析转录本的代码中人工编码这些变量中的一些的惯例是可能的。建议结合自动化程序和手动编码来捕捉这些变量或修改CLAN自动编码这些变量进行了讨论。
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.