Abstract

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.

Degree

MS

College and Department

David O. McKay School of Education; Communication Disorders

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2003

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd35

Keywords

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

Language

English

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