Predicting English Word Concreteness through Its Multidimensional Perceptual and Action Strength Norms
Saved in:
| Title: | Predicting English Word Concreteness through Its Multidimensional Perceptual and Action Strength Norms |
|---|---|
| Language: | English |
| Authors: | Mohsen Dolatabadi (ORCID |
| Source: | Australian Journal of Applied Linguistics. 2023 6(3):176-187. |
| Availability: | Castledown Publishers. Ground Level, 470 St Kilda Road, Melbourne, 3004, Australia. Tel: +61-3-7003-8355; e-mail: contact@castledown.com; Web site: https://castledown.online/journals/ajal/ |
| Peer Reviewed: | Y |
| Page Count: | 12 |
| Publication Date: | 2023 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Prediction, Validity, Models, Computational Linguistics, Psychomotor Skills, English, Word Frequency, Error of Measurement, Language Processing, Sensory Integration, Multiple Regression Analysis |
| ISSN: | 2209-0959 |
| Abstract: | 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.] |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1417092 |
| Database: | ERIC |
| Abstract: | 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.] |
|---|---|
| ISSN: | 2209-0959 |
