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Fast connectionist learning: words and case

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Abstract

Some basic principles of connectionist research are explained along with an account of a number of the techniques necessary for constructing connectionist models. The objective is to introduce the area to people with limited mathematical and computational backgrounds by reducing the examples to simple arithmetic. In this way, a solid basis will be provided for one of the learning algorithms that have been fundamental to the development of network learning: the Hebbian learning rule. After outlining the technique in detail, two examples are provided to make the ideas concrete. These are learning to associate visual features with words and learning case representations.

1. Of course, this is a very simple account of language representation, but it suffices our current purposes. We do not discuss more difficult problems such as prepositional attachment and recursion.

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References

  • Charniak, E. (1981) The case-slot identity theory. Cognitive Science, 5, 285–292.

    Google Scholar 

  • Fillmore, C.J. (1968) The case for case. In Universals in Linguistic Theory (Eds) E.Bach and R.Harms Holt, Rinehart, and Winston, New York.

    Google Scholar 

  • Fodor, J.A. and Pylyshyn, Z.W. (1988) Connectionism and cognitive architecture: a critical analysis. Cognition, 28, 3–72.

    Google Scholar 

  • Hebb, D.O. (1949) The Organization of Behavior. Wiley, New York.

    Google Scholar 

  • James, W. (1961) Psychology: Briefer Course. Harper, New York. (Originally published in 1892).

    Google Scholar 

  • McCulloch, W.S. & Pitts, W.H. (1943) A logical calculus of ideas imminent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.

    Google Scholar 

  • Sharkey, N.E. (1988) Neural network learning techniques. In Understanding Cognitive Science. (Ed.) M.McTear. Horwood, Chichester.

    Google Scholar 

  • Sharkey, N.E. (1989) A PDP learning approach to natural language understanding in Neural Computing Architectures: The Design of Brainlike Machines. (Ed.) I.Alexander. Kogan Page, London.

    Google Scholar 

  • Smolensky, P. (1987) On variable binding and the representation of symbolic structures in connectionist systems. Technical Report CU-CS-355-87, Department of Computer Science, University of Colorado at Boulder.

  • Waldeyer, H.W. (1891) Uber einige neuere forschungen im Gebiete der Anotomie des Centrainervensystems. Deutsche medizinische Wochenschrift. 17, 1352–1356.

    Google Scholar 

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Sharkey, N.E. Fast connectionist learning: words and case. Artif Intell Rev 3, 33–47 (1989). https://doi.org/10.1007/BF00139195

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