Abstract
Since the outbreak of connectionist modelling in the mid eighties, several problems in natural language processing have been tackled by employing neural network-based techniques. Neural network’s “biological plausibility” offers a promising framework in which the computational treatment of language may be linked to other disciplines such as cognitive science and psychology. With this brief survey, we set out to explore the landscape of artificial neural models for the acquisition of language that have been proposed in the research literature.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Quartz, S.R., Sejnowski, T.J.: The Neural Basis of Cognitive Development: A Constructivist Manifesto. Behavioural and Brain Sciences 20, 537–596 (1997)
Chomsky, N.: Aspects of the Theory of Syntax. MIT Press, Cambridge (1965)
Quinlan, P. T., van der Maas, H. L. J., Jansen, B. R. J., Booij, O., Rendell, M.: Re-Thinking Stages of Cognitive Development: An Appraisal of Connectionist Models of the Balance Scale Task. Cognition (2006) (in press)
MacKay, D.J.C.: Bayesian Methods for Back-propagation Networks. In: Domany, E., van Hemmen, J.L., Schulten, K. (eds.) Models of Neural Networks III, ch. 6. Springer, New York (1994)
Neal, R.: Bayesian Learning for Neural Networks, PhD thesis, University of Toronto, Canada (1994)
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)
Friston, K.: A Theory of Cortical Responses. Philosophical Transactions of the Royal Society, B 360, 815–836 (2005)
Rumelhart, D., McClelland, J.: On the Learning of the Past Tenses of English Verbs. Parallel distributed processing: Explorations in the microstructure of cognition 2, 216–271 (1986)
McClelland, J.L., Elman, J.L.: Interactive Processes in Speech Perception: The TRACE Model. Parallel distributed processing 2, 58–121 (1986)
Li, P., Farkas, I.: A Self-Organizing Connectionist Model of Bilingual Processing. In: Bilingual Sentence Processing, pp. 59–85 (2002)
Li, P., Farkas, I., MacWhinney, B.: Early Lexical Development in a Self-Organizing Neural Network. Neural Networks 17, 1345–1362 (2004)
Christiansen, M.H., Chater, N.: Connectionist Natural Language Processing: The State of the Art. Cognitive Science 23, 417–437 (1999)
Pinker, S., Prince, A.: On Language and Connectionism: Analysis of A Parallel Distributed Processing Model of Language Acquisition. Cognition 28, 73–193 (1988)
Garson, J.: Connectionism. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (2002)
Fodor, J.A., Pylyshyn, Z.: Connectionism and Cognitive Architecture: A Critical Analysis. Cognition 28, 3–71 (1988)
Pollack, J.B.: Recursive Distributed Representations. Artificial Intelligence 46, 77–105 (1990)
Miikkulainen, R.: Dyslexic and Category-Specific Impairments in a Self-Organizing Feature Map Model of the Lexicon. Brain and Language 59, 334–366 (1997)
MacWhinney, B.: The CHILDES Project: Tools for Analyzing Talk. Lawrence Erlbaum Associates, Mahwah (2000)
Kohonen, T.: Self-Organizing Maps. In: Springer Series in Information Sciences, vol. 30 (1995)
Elman, J.L.: Finding Structure in Time. Cognitive Science 14, 179–211 (1990)
Anderson, B.: Kohonen Neural Networks and Language. Brain and Language 70, 86–94 (1999)
Carpenter, G.A., Grossberg, S.: The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network. Computer 21(3), 77–88 (1988)
Yu, C., Ballard, D.H., Aslin, R.N.: The Role of Embodied Intention in Early Lexical Acquisition. Cognitive Science 29, 961–1005 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Poveda, J., Vellido, A. (2006). Neural Network Models for Language Acquisition: A Brief Survey. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_160
Download citation
DOI: https://doi.org/10.1007/11875581_160
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
eBook Packages: Computer ScienceComputer Science (R0)