Hybrid Computational Model for Producing English Past Tense Verbs
In this work, we explore the use of artificial neural networks (ANN) as computational models for producing English past tense verbs by combining them with the genetic algorithms (GA). The principal focus was to model the population variability exhibited by children in learning the past tense. This variability stems from genetic and environmental origins.We simulated the effects of genetic influences via variations in the neuro computational parameters of the ANNs, and the effects of environmental influences via a filter applied to the training set, implementing variation in the information available to the child produced by, for example, differences in socio-economic status. In the model, GA served two main purposes - to create the population of artificial neural networks and to encode the neuro computational parameters of the ANN into the genome. English past tense provides an interesting training domain in that it comprises a set of quasi-regular mappings. English verbs are of two types, regular verbs and irregular verbs. However, a similarity gradient also exists between these two classes. We consider the performance of the combination of ANN and GA under a range of metrics. Our tests produced encouraging results as to the utility of this method, and a foundation for future work in using a computational framework to capture population-level variability.
KeywordsFeed forward neural networks imbalanced datasets hamming distance nearest neighbour genetic algorithms English past tense verbs quasi regular mappings
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