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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Accuracy of Automated Grammatical Tagging of Narrative Language Samples from Spanish-Speaking Children

Harmon, Tyson Gordon 08 March 2012 (has links) (PDF)
The present study measured the accuracy of automated grammatical tagging software as compared to manual tagging in Spanish-speaking children's personal and fictional event narrative language samples. Studies have identified articles, clitic (contracted with a verb) pronouns, and verbs as clinical markers for language impairment in Spanish-speaking children. Automated grammatical tagging software may aid in the rapid identification of these grammatical markers. Grammatical morphemes of 30 first and fourth grade children's personal and fictional event narrative samples were tagged and compared with their respective manually tagged samples. The accuracy of word-level coding averaged 91%, and similar accuracy was found for clinically significant tags. Automated grammatical analysis has the potential to accurately identify clinically relevant grammatical forms in samples from children who speak Spanish.
2

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

Millet, Deborah 01 January 2003 (has links) (PDF)
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.

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