Assessing naming errors using an automated machine learning approach.
Assessing naming errors using an automated machine learning approach.
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DOI：
10.1037/neu0000860
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发表时间：
2022-11
期刊：
影响因子：
2.4
通讯作者：
Lei, Chia-Ming
中科院分区：
文献类型：
--
作者：
Schnur, Tatiana T.;Lei, Chia-Ming
左半球中风后，20-50%的人会出现语言障碍，包括命名困难。排除与预期目标语义相关的错误（例如，产生“小提琴”的图片HARP）表明，在获取知识的单词形式及其含义的潜在障碍。了解命名障碍的原因对于更好地建模语言产生以及定制个性化康复至关重要。然而，命名错误的评估通常是主观和费力的二分分类。因此，这些评价没有捕捉到语义相似度，并且由于主观性而容易受到较低的评分者间可靠性的影响。我们调查了是否计算语言的措施，使用word 2 vec解决这些限制，通过评估错误的对象命名在一组患者在急性期的左半球中风（N=105）。皮尔逊相关性证明了word 2 vec的命名错误语义相关估计和词汇语义知识获取独立测试的良好收敛效度（p < .0001）。此外，多元回归分析显示，word 2 vec的语义相关估计在预测词汇语义知识测试的表现方面明显优于人类错误分类。有用的理论家和临床医生，我们的word 2 vec为基础的方法提供了一个自动化的，连续的，客观的心理测量访问词汇语义知识在命名。
After left hemisphere stroke, 20–50% of people experience language deficits, including difficulties in naming. Naming errors that are semantically related to the intended target (e.g., producing “violin” for picture HARP) indicate a potential impairment in accessing knowledge of word forms and their meanings. Understanding the cause of naming impairments is crucial to better modeling of language production as well as for tailoring individualized rehabilitation. However, evaluation of naming errors is typically by subjective and laborious dichotomous classification. As a result, these evaluations do not capture the degree of semantic similarity and are susceptible to lower inter-rater reliability because of subjectivity. We investigated whether a computational linguistic measure using word2vec addressed these limitations by evaluating errors during object naming in a group of patients during the acute stage of a left-hemisphere stroke (N=105). Pearson correlations demonstrated excellent convergent validity of word2vec’s semantically related estimates of naming errors and independent tests of access to lexical-semantic knowledge (p < .0001). Further, multiple regression analysis showed word2vec’s semantically related estimates were significantly better than human error classification at predicting performance on tests of lexical-semantic knowledge. Useful to both theorists and clinicians, our word2vec-based method provides an automated, continuous, and objective psychometric measure of access to lexical-semantic knowledge during naming.
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影响因子：
2.6
作者：
Fergadiotis, Gerasimos;Gorman, Kyle;Bedrick, Steven
通讯作者：
Bedrick, Steven
影响因子：
2.2
作者：
Boyle, Mary
通讯作者：
Boyle, Mary
影响因子：
3.7
作者：
Brodeur MB;Dionne-Dostie E;Montreuil T;Lepage M
通讯作者：
Lepage M
影响因子：
2.6
作者：
Drew, RL;Thompson, CK
通讯作者：
Thompson, CK
DOI：
10.1080/14640749508401426
发表时间：
1995-11-01
期刊：
QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY SECTION A-HUMAN EXPERIMENTAL PSYCHOLOGY
影响因子：
--
作者：
HOWARD, D
通讯作者：
HOWARD, D