Abstract

By the later preschool years, most children have a knowledge of the grammatical categories of their native language and are capable of expanding this knowledge to novel words. To model this accomplishment, researchers have created a variety of explicit, testable models or algorithms. These have had partial but promising success in extracting grammatical word categories from transcriptions of caregiver input to young children. Additional insight into children's learning of the grammatical categories of words might be gained from evolutionary computing algorithms, which apply principles of evolutionary biology such as variation, adaptive change, self-regulation, and inheritance to computational models. The current thesis applied such a model to the language addressed to five children, whose ages ranged from 1;1 to 5;1 (years;months). The model evolved dictionaries linking words to their grammatical tags and was run for 4000 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 averaged 92.74%. Directions for further development and evaluation of the model are proposed.

Degree

College and Department

David O. McKay School of Education; Communication Disorders

Rights

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