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Simulating Developmental Changes in Noun Richness through Performance-limited Distributional Analysis
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Defaulting effects contribute to the simulation of cross-linguistic differences in Optional Infinitive errors
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Sinuosity and the affect grid: A method for adjusting repeated mood scores
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Cluster damage robustness analysis and space independent community detection in complex networks
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Gegov, Emil. - : Brunel University School of Engineering and Design PhD Theses, 2012
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Transition expertise: Cognitive factors and developmental processes that contribute to repeated successful career transitions amongst elite athletes, musicians and business people
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Modelling language acquisition in children using network theory
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In: European Perspectives on Cognitive Sciences (2011)
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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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Simulating the referential properties of Dutch, German and English Root Infinitives in MOSAIC
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Does chess need intelligence? – A study with young chess players
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Modelling the developmental patterning of finiteness marking in English, Dutch, German and Spanish using MOSAIC
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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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On the resolution of ambiguities in the extraction of syntactic categories through chunking
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Abstract:
In recent years, several authors have investigated how co-occurrence statistics in natural language can act as a cue that children may use to extract syntactic categories for the language they are learning. While some authors have reported encouraging results, it is difficult to evaluate the quality of the syntactic categories derived. It is argued in this paper that traditional measures of accuracy are inherently flawed. A valid evaluation metric needs to consider the wellformedness of utterances generated through a production end. This paper attempts to evaluate the quality of the categories derived from co-occurrence statistics through the use of MOSAIC, a computational model of syntax acquisition that has already been used to simulate several phenomena in child language. It is shown that derived syntactic categories that may appear to be of high quality quickly give rise to errors that are not typical of child speech. A solution to this problem is suggested in the form of a chunking mechanism that serves to differentiate between alternative grammatical functions of identical word forms. Results are evaluated in terms of the error rates in utterances produced by the system as well as the quantitative fit to the phenomenon of subject omission.
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Keyword:
Chunking; Co-occurrence statistics; Cognitive modelling; Distributional learning; Language acquisition; MOSAIC; Syntactic categories
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URL: http://www.sciencedirect.com/science/article/pii/S1389041704000610 http://bura.brunel.ac.uk/handle/2438/802
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