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- W3119411963 abstract "This study explores the self-organizing neural network as a model of lexical and morphological acquisition. We examined issues of generalization, representation, and recovery in a multiple feature-map model. Our results indicate that self-organization and Hebbian learning are two important computational principles that can account for the psycho- linguistic processes of semantic representation, morphological generalization, and recovery from generalizations in the acquisition of reversive prefixes such as un- and dis-. These results attest to the utility of self-organizing neural networks in the study of language acquisition." @default.
- W3119411963 created "2021-01-18" @default.
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- W3119411963 date "2020-12-22" @default.
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- W3119411963 title "Generalization, Representation, and Recovery in a Self-Organizing Feature-Map Model of Language Acquisition" @default.
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- W3119411963 doi "https://doi.org/10.4324/9781410603494-59" @default.
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