Journal of Undergraduate Research


re-ordering utterances, transition probabilities, grammatical tags, language acquisition


David O. McKay School of Education


Communication Disorders


It was our desire to investigate further, using a computer model, how children acquire language. Specifically, we decided to investigate how children learn how to arrange grammatical tags (i.e. grammatical categories: verb, adjective, etc.) into the proper order. Originally, we were going to investigate how an evolutionary algorithm could improve the degree of accuracy in re-ordering grammatical tags. However, we decided to branch off of a previous study to gain a better understanding of the potential of a computer model to re-order the grammatical tags with just the tag transition probabilities. In her thesis last year, Katie Shaw Walker, a graduate student, used 8 child/adult samples with this question in mind. The computer model was trained with the statistical data from the adult utterances and then was tested on the child’s utterances. Each word in Katie’s study had its most likely grammatical category assigned to each word. The findings were that the model could re-order the child’s utterances with an 80-90% accuracy. For our study, each word was randomly assigned a grammatical category. This divergence was intended to give us a better understanding as to how the computer program would do with randomly assigned tags. We were surprised to find that the program could already achieve higher than chance results simply by learning from the parent’s utterances.

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