Abstract
In recent years connectionist researchers have demonstrated many examples of holistic symbol processing, where symbolic structures are operated upon as a whole by a neural network, by using a connectionist compositional representation of the structures. In this paper the ability of the Sequential RAAM (SRAAM) to generate representations that support holistic symbol processing is evaluated by attempting to perform Chalmers’ syntactic transformations using it. It is found that the SRAAM requires a much larger hidden layer for this task than the RAAM and that it tends to cluster its hidden layer states close together, leading to a representation that is fragile to noise. The lessons for connectionism and holistic symbol processing are discussed and possible methods for improving the SRAAM’s performance suggested.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Balogh, I.L.: An analysis of a connectionist internal representation: Do RAAM networks produce truly distributed representations? PhD thesis, New Mexico State University (1994)
Blank, D.S., Meeden, L.A., Marshall, J.B.: Exploring the symbolic/subsymbolic continuum: A case study of RAAM. In: Dinsmore, J. (ed.) Symbolic and Connectionist Paradigms: Closing the Gap, pp. 113–148. Lawrence Erlbaum Associates, Hillsdale (1992)
Callan, R.E., Palmer-Brown, D.: (S)RAAM: An analytical technique for fast and reliable derivation of connectionist symbol structure representations. Connection Science 9(2), 139–159 (1997)
Chalmers, D.J.: Syntactic transformations on distributed representations. Connection Science 2(1–2), 53–62 (1990); Reprinted in [16], pp. 46–55
Chrisman, L.: Learning recursive distributed representations for holistic computation. Connection Science 3(4), 345–366 (1991)
French, R.M.: Dynamically constraining connectionist networks to produce distributed, orthogonal representations to reduce catastrophic interference. In: Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, Atlanta, GA, pp. 335–340. Lawrence Erlbaum Associates, Hillsdale (1994)
Hammerton, J.A.: Exploiting Holistic Computation: An evaluation of the Sequential RAAM. PhD thesis, School of Computer Science, The University of Birmingham, UK (1998)
Hammerton, J.A.: Holistic computation: Reconstructing a muddled concept. Connection Science 10(1), 3–19 (1998)
Ho, E.K.S., Chan, L.W.: Confluent preorder parser as a finite state automata. In: Proceedings of International Conference on Artificial Neural Networks ICANN 1996, Bochum, Germany, pp. 899–904. Springer, Berlin (1996)
Kalman, B.L., Kwasny, S.C.: High performance training of feedforward and simple recurrent networks. Neurocomputing 14, 63–83 (1997)
Kohonen, T.: The self-organising map. Proceedings of the IEEE 78(9), 1464–1480 (1990)
Kwasny, S.C., Kalman, B.L.: Tail-recursive distributed representations and simple recurrent networks. Connection Science 7(1), 61–80 (1995)
Niklasson, L., Sharkey, N.E.: Systematicity and generalization in compositional connectionist representations. In: Dorffner, G. (ed.) Neural Networks and a New Artificial Intelligence, pp. 217–232. Thomson Computer Press, London (1997)
Plate, T.A.: Distributed Representations and Nested Compositional Structure. PhD thesis, University of Toronto (1994)
Pollack, J.B.: Recursive distributed representations. Artificial Intelligence 46(1-2), 77–105 (1990)
Sharkey, N.E. (ed.): Connectionist Natural Language Processing: Readings from Connection Science. Intellect, Oxford (1992)
Sperduti, A.: Labeling RAAM. Technical Report TR-93-029, International Computer Science Institute, Berkeley, California (1993)
Sperduti, A.: On Some Stability Properties of the LRAAM Model. Technical Report TR-93-031, International Computer Science Institute, Berkeley, California (1993)
Weber, V.: Connectionist unification with a distributed representation. In: Proceedings of the International Joint Conference on Neural Networks– IJCNN 1992, Beijing, China, pp. 555–560. IEEE, Piscataway (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hammerton, J.A., Kalman, B.L. (2000). Holistic Symbol Processing and the Sequential RAAM: An Evaluation. In: Wermter, S., Sun, R. (eds) Hybrid Neural Systems. Hybrid Neural Systems 1998. Lecture Notes in Computer Science(), vol 1778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10719871_21
Download citation
DOI: https://doi.org/10.1007/10719871_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67305-7
Online ISBN: 978-3-540-46417-4
eBook Packages: Springer Book Archive