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Comparing MOSAIC and the variational learning model of the optional infinitive stage in early child language
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On the Utility of Conjoint and Compositional Frames and Utterance
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Understanding the Developmental Dynamics of Subject Omission: The Role of Processing Limitations in Learning
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Simulating the Noun-Verb Asymmetry in the Productivity of Children’s Speech
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Linking working memory and long-term memory: A computational model of the learning of new words
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Jones, G; Gobet, F; Pine, J M. - : Blackwell Publishing. The definitive version is available at onlinelibrary.wiley.com, 2007
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Modelling the Development of Children’s use of Optional Infinitives in Dutch and English using MOSAIC
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Unifying cross-linguistic and within-language patterns of finiteness marking in MOSAIC
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Simulating the cross-linguistic development of optional infinitive errors in MOSAIC.
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Simulating optional infinitive errors in child speech through the omission of sentence-internal elements.
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Resolving ambiguities in the extraction of syntactic categories through chunking.
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Simulating the temporal reference of Dutch and English Root Infinitives.
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Modelling syntactic development in a cross-linguistic context
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The role of input size and generativity in simulating language acquisition.
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Modelling children's negation errors using probabilistic learning in MOSAIC.
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Modelling the development of Dutch Optional Infinitives in MOSAIC.
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Subject omission in children's language; The case for performance limitations in learning.
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Modeling children’s case marking errors with MOSAIC
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Abstract:
We present a computational model of early grammatical development which simulates case-marking errors in children’s early multi-word speech as a function of the interaction between a performance-limited distributional analyser and the statistical properties of the input. The model is presented with a corpus of maternal speech from which it constructs a network consisting of nodes which represent words or sequences of words present in the input. It is sensitive to the distributional properties of items occurring in the input and is able to create ‘generative’ links between words which occur frequently in similar contexts, building pseudo-categories. The only information received by the model is that present in the input corpus. After training, the model is able to produce child-like utterances, including case-marking errors, of which a proportion are rote-learned, but the majority are not present in the maternal corpus. The latter are generated by traversing the generative links formed between items in the network.
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Keyword:
acquisition of language; case-marking errors; CHILDES; chunking; discrimination net; distributional analysis; MOSAIC; syntax
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URL: http://bura.brunel.ac.uk/handle/2438/2121
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Modeling the optional infinite stage in MOSAIC: A generalization to Dutch
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