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- W2123751431 abstract "Neural networks, or the so-called connectionist networks, provide a basis for studying child language development in that such networks emphasise learning, either from observations or from being told. We report a neural network based simulation of aspects of child language development, essentially focusing on child language at the one-word stage. We simulate the learning of associations between the so-called conceptual relations, perceptual entities and functional words, leading to the production of child-like one-word utterances. The simulations are carried out using ACCLAIM - A Connectionist Child LAnguage development and Imitation Model. ACCLAIM is a ‘hybrid’ neural network architecture, systematically synthesising both supervised and unsupervised learning networks, thus accounting for the diverse nature of inputs to and outputs from a child learning language. The simulations carried out are ‘language informed’ as realistic child language data has been used for training the neural networks." @default.
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- W2123751431 date "1996-01-01" @default.
- W2123751431 modified "2023-09-27" @default.
- W2123751431 title "A Neural Network Simulation of Child Language Development at the One-word Stage" @default.
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