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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 the optional infinite stage in MOSAIC: A generalization to Dutch
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Abstract:
This paper presents a model of a stage in children’s language development known as the optional infinitive stage. The model was originally developed for English, where it was shown to provide a good account of several phenomena. The model, which uses a discrimination network, analyzes the distribution of words in the input, and derives word classes from them by linking words that are used in a similar context. While the earlier version of the model is sensitive only to characteristics of phrases that follow target words, the present version also takes preceding input into consideration. Also, the present version uses a probabilistic rather than a deterministic learning mechanism. Generalisation of the model to Dutch is considered a strong test of the model, since Dutch displays the optional infinitive phenomenon, while its syntax differs substantially from that of English. The model was presented with child-directed input from two Dutch mothers, and its output was compared to that of the respective children. Despite the fact that the model was developed for a different language, it captures the optional infinitive phenomenon in Dutch as it does in English, while showing sensitivity to Dutch syntax. These results suggest that a simple distributional analyzer can capture the regularities of different languages despite the apparent differences in their syntax.
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Keyword:
acquisition of language; distributional analyser; Dutch; English; MOSAIC; optional infinitive; probabilistic learning; syntax
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URL: http://bura.brunel.ac.uk/handle/2438/2133
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