Abstract

Most children who follow a typical developmental timeline learn the grammatical categories of words in their native language by the time they enter school. Researchers have worked to provide a number of explicit, testable models or algorithms in an attempt to model this language development. These models or algorithms have met with some varying success in terms of determining grammatical word categories from the transcripts of adult input to children. A new model of grammatical category acquisition involving an application of evolutionary computing algorithms may provide further understanding in this area. This model implements aspects of evolutionary biology, such as variation, adaptive change, self-regulation, and inheritance. The current thesis applies this model to six English language corpora. The model created dictionaries based on the words in each corpus and matched the words with their grammatical tags. The dictionaries evolved over 5,000 generations. Four different mutation rates were used in creating offspring dictionaries. The accuracy achieved by the model in correctly matching words with tags reached 90%. Considering this success, further research involving an evolutionary model appears warranted.

Degree

MS

College and Department

David O. McKay School of Education; Communication Disorders

Rights

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

Date Submitted

2014-07-01

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd7148

Keywords

grammatical word categories, evolutionary programming, language acquisition

Language

English

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