View in EDS

Predicting English Word Concreteness through Its Multidimensional Perceptual and Action Strength Norms

Saved in:
Bibliographic Details
Title: Predicting English Word Concreteness through Its Multidimensional Perceptual and Action Strength Norms
Language: English
Authors: Mohsen Dolatabadi (ORCID 0009-0002-8062-4050)
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
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=EJ1417092
    Name: ERIC Full Text
    Category: fullText
    Text: Full Text from ERIC
Header DbId: eric
DbLabel: ERIC
An: EJ1417092
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Predicting English Word Concreteness through Its Multidimensional Perceptual and Action Strength Norms
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Mohsen+Dolatabadi%22">Mohsen Dolatabadi</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0002-8062-4050">0009-0002-8062-4050</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Australian+Journal+of+Applied+Linguistics%22"><i>Australian Journal of Applied Linguistics</i></searchLink>. 2023 6(3):176-187.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: 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/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 12
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Validity%22">Validity</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+Linguistics%22">Computational Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Psychomotor+Skills%22">Psychomotor Skills</searchLink><br /><searchLink fieldCode="DE" term="%22English%22">English</searchLink><br /><searchLink fieldCode="DE" term="%22Word+Frequency%22">Word Frequency</searchLink><br /><searchLink fieldCode="DE" term="%22Error+of+Measurement%22">Error of Measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Processing%22">Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Sensory+Integration%22">Sensory Integration</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+Regression+Analysis%22">Multiple Regression Analysis</searchLink>
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2209-0959
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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.]
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1417092
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1417092
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 176
    Subjects:
      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Validity
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Computational Linguistics
        Type: general
      – SubjectFull: Psychomotor Skills
        Type: general
      – SubjectFull: English
        Type: general
      – SubjectFull: Word Frequency
        Type: general
      – SubjectFull: Error of Measurement
        Type: general
      – SubjectFull: Language Processing
        Type: general
      – SubjectFull: Sensory Integration
        Type: general
      – SubjectFull: Multiple Regression Analysis
        Type: general
    Titles:
      – TitleFull: Predicting English Word Concreteness through Its Multidimensional Perceptual and Action Strength Norms
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Mohsen Dolatabadi
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-electronic
              Value: 2209-0959
          Numbering:
            – Type: volume
              Value: 6
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Australian Journal of Applied Linguistics
              Type: main
ResultId 1