ERIC Number: EJ1417092
Record Type: Journal
Publication Date: 2023
Pages: 12
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: EISSN-2209-0959
Predicting English Word Concreteness through Its Multidimensional Perceptual and Action Strength Norms
Australian Journal of Applied Linguistics, v6 n3 p176-187 2023
Many datasets resulting from participant ratings for word norms and also concreteness ratios are available. However, the concreteness information of infrequent words and non-words is rare. This work aims to propose a model for estimating the concreteness of infrequent and new lexicons. Here, we used Lancaster sensory-motor word norms to predict the word concreteness ratios of an English word dataset. After removing the missing values, we employed a stepwise multiple linear regression (SW-MLR) procedure for choosing an optimum number of norms to develop a predictive multiple regression model. Finally, we validate our model using 10-fold cross-validation. The final model could predict concreteness by Residual Mean Standard Error equal to 0.723 and R-Square of 0.515. Also, our results showed that all 11 variables of this dataset except the Head-mouth parameter are useful predictors. In conclusion, as a growing demand to know the concreteness values of non-words and also infrequent words is evident, our statistical method can pave the way for controlled experiments when choosing non-words as a stimulus is critical. [Note: The publication year (2024) shown on the PDF is incorrect. The correct year of publication is 2023.]
Descriptors: Prediction, Validity, Models, Computational Linguistics, Psychomotor Skills, English, Word Frequency, Error of Measurement, Language Processing, Sensory Integration, Multiple Regression Analysis
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A