Automated Grammatical Tagging of Language Samples from Children with and without Language Impairment

Grammatical classification ("tagging") of words in language samples is a component of syntactic analysis for both clinical and research purposes. Previous studies have shown that probability-based software can be used to tag samples from adults and typically-developing children with high (about 95%) accuracy. The present study found that similar accuracy can be obtained in tagging samples from school-aged children with and without language impairment if the software uses tri-gram rather than bi-gram probabilities and large corpora are used to obtain probability information to train the tagging software.

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Millet, Deborah

• automated grammatical tagging
• grammatical classification
• language samples
• syntactic analysis
• language impairment
• DSS
• developmental sentence scoring
• Language Assessment
• Remediation and Screening Procedure
• LARSP
• bi-gram probability
• tri-gram probability
• Communication Sciences and Disorders

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/1139

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-2138