从抄本中计算核心词汇的自动化程序验证。
Validation of an Automated Procedure for Calculating Core Lexicon From Transcripts.
机构信息
Marquette University, Milwaukee, WI.
Indiana University, Bloomington.
出版信息
J Speech Lang Hear Res. 2022 Aug 17;65(8):2996-3003. doi: 10.1044/2022_JSLHR-21-00473. Epub 2022 Aug 2.
PURPOSE
The aim of this study was to advance the use of structured, monologic discourse analysis by validating an automated scoring procedure for core lexicon (CoreLex) using transcripts.
METHOD
Forty-nine transcripts from persons with aphasia and 48 transcripts from persons with no brain injury were retrieved from the AphasiaBank database. Five structured monologic discourse tasks were scored manually by trained scorers and via automation using a newly developed CLAN command based upon previously published lists for CoreLex. Point-to-point (or word-by-word) accuracy and reliability of the two methods were calculated. Scoring discrepancies were examined to identify errors. Time estimates for each method were calculated to determine if automated scoring improved efficiency.
RESULTS
Intraclass correlation coefficients for the tasks ranged from .998 to .978, indicating excellent intermethod reliability. Automated scoring using CLAN represented a significant time savings for an experienced CLAN user and for inexperienced CLAN users following step-by-step instructions.
CONCLUSIONS
Automated scoring of CoreLex is a valid and reliable alternative to the current gold standard of manually scoring CoreLex from transcribed monologic discourse samples. The downstream time saving of this automated analysis may allow for more efficient and broader utilization of this discourse measure in aphasia research. To further encourage the use of this method, go to https://aphasia.talkbank.org/discourse/CoreLexicon/ for materials and the step-by-step instructions utilized in this project.
SUPPLEMENTAL MATERIAL
目的
本研究旨在通过验证基于语料库的核心词汇（CoreLex）的自动评分程序，推进使用结构化的独白话语分析。
方法
从 AphasiaBank 数据库中检索了 49 份失语症患者和 48 份无脑损伤患者的转录本。对 5 种结构化的独白话语任务进行了手动评分和自动化评分，自动化评分使用了一种新开发的基于先前发布的 CoreLex 列表的 CLAN 命令。计算了两种方法的点对（或逐字）准确性和可靠性。检查了评分差异，以确定错误。计算了每种方法的时间估计值，以确定自动化评分是否提高了效率。
结果
任务的组内相关系数范围为.998 至.978，表明两种方法之间具有极好的可靠性。对于有经验的 CLAN 用户和按照逐步说明操作的无经验的 CLAN 用户，使用 CLAN 进行自动评分可显著节省时间。
结论
使用 CLAN 对 CoreLex 进行自动评分是一种有效的、可靠的替代方法，可以替代当前从转录的独白话语样本中手动评分 CoreLex 的黄金标准。这种自动分析的下游时间节省可能会使这种话语测量在失语症研究中得到更有效的、更广泛的利用。要进一步鼓励使用这种方法，请访问 https://aphasia.talkbank.org/discourse/CoreLexicon/ 以获取本项目中使用的材料和逐步说明。
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