Abstract
This paper presents a self-organizing, real-time, hierarchical neural network model of sequential processing, and shows how it can be used to induce recognition codes corresponding to word categories and elementary grammatical structures. The model, first introduced in Mannes (1992), learns to recognize, store, and recall sequences of unitized patterns in a stable manner, either using short-term memory alone, or using long-term memory weights. Memory capacity is only limited by the number of nodes provided. Sequences are mapped to unitized patterns, making the model suitable for hierarchical operation. By using multiple modules arranged in a hierarchy and a simple mapping between output of lower levels and the input of higher levels, the induction of codes representing word category and simple phrase structures is an emergent property of the model. Simulation results are reported to illustrate this behavior.
This research was partially supported by NSF grant # IRI-9024877. The author would like to thank Daniel Bollock, Gall Carpenter, Michael Cohen, and Stephen Grossberg, for their valuable advice and support.
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© 1993 Springer-Verlag Berlin Heidelberg
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Mannes, C. (1993). Self-organizing grammar induction using a neural network model. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_147
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DOI: https://doi.org/10.1007/3-540-56798-4_147
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