Abstract

Most typically developing children have achieved a knowledge of the grammatical categories of the words in their native language by school age. To model this achievement, researchers have developed a variety of explicit, testable models or algorithms which have had partial but promising success in extracting the grammatical word categories from the transcriptions of caregiver input to children. Additional insight into children's learning of the grammatical categories of words might be obtained from an application of evolutionary computing algorithms, which simulate principles of evolutionary biology such as variation, adaptive change, self-regulation, and inheritance. Thus far, however, this approach has only been applied to English language corpora. The current thesis applied such a model to corpora of language addressed to five Spanish-speaking children, whose ages ranged from 0;11 to 4;8 (years; months). The model evolved dictionaries which linked words to their grammatical tags and was run for 5000 cycles; four different rates of mutation of offspring dictionaries were assessed. The accuracy for coding the words in the corpora of language addressed to the children peaked at about 85%. Directions for further development and evaluation of the model and its application to Spanish language corpora are suggested.

Degree

College and Department

David O. McKay School of Education; Communication Disorders

Rights

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