Connectionist learning of regular graph grammars

Connection Science

Volumn 13, Issue 2, 2001, Pages 127-188

  Fletcher, Peter ^a  

^a KEELE UNIVERSITY   (United Kingdom)

Author keywords

Grammatical inference; Graph grammars; Neural networks; Parallel parsing; Regular grammars; Stochastic grammars; Symbol processing; Unsupervised learning

Indexed keywords

NODE SPLITTING;

ALGORITHMS; GRAPH THEORY; ITERATIVE METHODS; NEURAL NETWORKS; RECURSIVE FUNCTIONS;

COMPUTATIONAL GRAMMARS;

EID: 0035537629     PISSN: 09540091     EISSN: None     Source Type: Journal    
DOI: 10.1080/09540090110072327     Document Type: Article

References (94)

1. Angluin, D., 1978, On the complexity of minimum inference of regular sets. Information and Control, 39: 337-350.
2. Angluin, D., 1987, Learning regular sets from queries and counterexamples. Information and Computation, 75 (2): 87-106.
3. Angluin, D., 1988, Queries and concept learning. Machine Learning, 2: 319-342.
4. Angluin, D., 1992, Computational learning theory: survey and selected bibliography. In Proceedings of the 24th Annual ACM Symposium on Theory of Computing (New York: ACM Press), pp. 351-369.
5. Angluin, D., and Kharitonov, M., 1991, When won't membership queries help? In Proceedings of the 23rd Annual ACM Symposium on Theory of Computing (New York: ACM Press), pp. 444-454.
6. Barnden, J. A., and Pollack, J. B., 1991, Problems for high-level connectionism. In J. A. Barnden and J. B. Pollack (eds) High-level Connectionist Models. Advances in Connectionist and Neural Computation Theory, Vol. 1 (Norwood, NJ: Ablex), pp. 1-16.
7. Bartsch-Spörl, B., 1983, Grammatical inference of graph grammars for syntactic pattern recognition. Lecture Notes in Computer Science, 153: 1-7.
8. Bauderon, M., 1996, A category-theoretical approach to vertex replacement: the generation of infinite graphs. Lecture Notes in Computer Science, 1073: 27-37.
9. Bell, T. C., Cleary, J. G., and Witten, I. H., 1990, Text Compression (Englewood Cliffs, NJ: Prentice-Hall).
10. Bhattacharyya, P., and Nagaraja, G., 1993, Learning a class of regular languages in the probably approximately correct learnability framework of Valiant. In Grammatical Inference: Theory, Applications and Alternatives, First Colloquium on Grammatical Inference, Essex, UK, 22-23 April 1993, IEE Digest No. 1993/092 (London: IEE).
11. Brandenburg, F. J., and Chytel, M. P., 1991, Cycle chain code picture languages. Lecture Notes in Computer Science, 532: 157-173.
12. Brandenburg, F. J., and Skodinis, K., 1996, Graph automata for linear graph languages. Lecture Notes in Computer Science, 1073: 336-350.
13. Browne, A., 1998, Performing a symbolic inference step on distributed representations. Neurocomputing, 19 (1-3): 23-34.
14. Bunke, H., and Haller, B., 1992, Syntactic analysis of context-free plex languages for pattern recognition. In H. S. Baird, H. Bunke and K. Yamamoto (eds) Structured Document Image Analysis (Berlin: Springer), pp. 500-519.
15. Carrasco, R. C., Oncina, J., and Calera, J., 1998, Stochastic inference of regular tree languages. Lecture Notes in Artificial Intelligence, 1433: 187-198.
16. Castano, M. A., Vidal, E., and Casacuberta, F., 1995, Finite-state automata and connectionist machines: a survey. Lecture Notes in Computer Science, 930: 433-440.
17. Charniak, E., and Santos, E., 1991, A context-free connectionist parser which is not connectionist, but then it is not really context-free either. In J. A. Barnden and J. B. Pollack (eds) High-level Connectionist Models, Advances in Connectionist and Neural Computation Theory, Vol. 1 (Norwood, NJ: Ablex), pp. 123-134.
18. Corbí, A., Oncina, J., and García, P., 1993, Learning regular languages from a complete sample by error correcting techniques. In Grammatical Inference: Theory, Applications and Alternatives, First Colloquium on Grammatical Inference, Essex, UK, 22-23 April 1993, IEE Digest No. 1993/092 (London: IEE).
19. Cottrell, G. W., 1989, A Connectionist Approach to Word Sense Disambiguation (London: Pitman).
20. Dányi, G., 1993, Regular inference with maximal valid grammar method. In Grammatical Inference: Theory, Applications and Alternatives, First Colloquium on Grammatical Inference, Essex, UK, 22-23 April 1993, IEE Digest No. 1993/092 (London: IEE).
21. Das, R., Giles, C. L., and Sun, G. Z., 1993, Using prior knowledge in a NNPDA to learn context-free languages. In S. J. Hanson, J. D., Cowan and C. L. Giles (eds) Advances in Neural Information Processing Systems 5 (San Mateo, CA: Morgan Kaufmann).
22. de la Higuera, C., 1998, Learning stochastic finite automata from experts. Lecture Notes in Artificial Intelligence, 1433: 79-89.
23. Dinsmore, J. (ed.), 1992, The Symbolic and Connectionist Paradigms: Closing the Gap (Hillsdale, NJ: Lawrence Erlbaum Associates).
24. Drewes, F., and Kreowski, H.-J., 1991, A note on hyperedge replacement. Lecture Notes in Computer Science, 532: 1-11.
25. Dupont, P., 1996, Incremental regular inference. Lecture Notes in Artificial Intelligence, 1147: 222-237.
26. Dupont, P., Miclet, L., and Vidal, E., 1994, What is the search space of the regular inference? Lecture Notes in Artificial Intelligence, 862: 25-37.
27. Ehrig, H., 1979, Introduction to the algebraic theory of graph grammars (a survey). Lecture Notes in Computer Science, 73: 1-69.
28. Ehrig, H., Korff, M. and Löwe, M., 1991. Tutorial introduction to the algebraic approach of graph grammars based on double and single pushouts. Lecture Notes in Computer Science, 532: 24-37.
29. Elman, J. L., 1990, Finding structure in time. Cognitive Science, 14 (2): 179-212.
30. Elman, J. L., 1992, Grammatical structure and distributed representations. In S. Davis (ed.) Connectionism: Theory and Practice (New York: Oxford University Press), pp. 138-178.
31. Engelfriet, J., and Rozenberg, G., 1991, Graph grammars based on node rewriting: an introduction to NLC graph grammars. Lecture Notes in Computer Science, 532: 12-23.
32. Fahlman, S. E., 1991, The Recurrent Cascade-Correlation Architecture. CMU-CS-91-100. School of Computer Science, Carnegie Mellon University.
33. Fanty, M., 1985, Context-free Parsing in Connectionist Networks, TR174, Computer Science Department, University of Rochester.
34. Flasiński, M., 1993, On the parsing of deterministic graph languages for syntactic pattern recognition. Pattern Recognition, 26 (1): 1-16.
35. Fletcher, P., 1991, A self-configuring network. Connection Science, 3 (1): 35-60.
36. Fletcher, P., 1992, Principles of node growth and node pruning. Connection Science, 4 (2): 125-141.
37. Fletcher, P., 1993, Neural networks for learning grammars. In Grammatical Inference: Theory, Applications and Alternatives, First Colloquium on Grammatical Inference, Essex, UK, 22-23 April 1993, IEE Digest No. 1993/092 (London: IEE).
38. Fletcher, P., 2000, The foundations of connectionist computation. Connection Science, 12 (2): 163-196.
39. Fodor, J. A., 1975, The Language of Thought (Cambridge, MA: Harvard University Press).
40. Fodor, J. A., and Pylyshyn, Z. W., 1988, Connectionisin and cognitive architecture: a critical analysis. Cognition, 28 (1, 2): 3-71.
41. Fu, K.-S., and Booth, T. L., 1975, Grammatical inference: introduction and survey, parts I & II. IEEE Transactions on Systems, Man, and Cybernetics, SMC-5: 95-111, 409-423.
42. García, P., and Vidal, E., 1990, Inference of k-testable languages in the strict sense and application to syntactic pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12: 920-925.
43. Garfield, J. L., 1997, Mentalese not spoken here: computation, cognition and causation. Philosophical Psychology, 10 (4): 413-435.
44. Giles, C. L., Miller, C. B., Chen, D., Chen, H. H., Sun, G. Z., and Lee, Y. C., 1992, Learning and extracting finite state automata with second-order recurrent neural networks. Neural Computation, 4: 393-405.
45. Giles, C. L., and Omlin, C. W., 1992, Inserting rules into recurrent neural networks. In S. Y. Kung, F. Fallside, J. A. Sorenson and C. A. Kamm (eds) Neural Networks for Signal Processing II, Proceedings of the 1992 IEEE-SP Workshop (New York: IEEE), pp. 13-22.
46. Ginsborg, S., 1966, The Mathematical Theory of Context-free Languages (New York: McGraw-Hill).
47. Giordano, J.-Y., 1994, Inference of context-free grammars by enumeration: structural containment as an ordering bias. Lecture Notes in Artificial Intelligence, 862: 212-221.
48. Gold, E. M., 1967, Language identification in the limit. Information and Control, 10: 447-474.
49. Gold, E. M., 1978, Complexity of automaton identification from given data. Information and Control, 37: 302-320.
50. Goldblatt, R., 1984, Topoi: The Categorial Analysis of Logic (Amsterdam: North-Holland).
51. Gregor, J., and Thomason, M. G., 1996, A disagreement count scheme for inference of constrained Markov networks. Lecture Notes in Artificial Intelligence, 1147: 168-178.
52. Habel, A., 1992, Hyperedge Replacement: Grammars and Languages (Berlin: Springer).
53. Hadley, R. F., 1999, Connectionism and novel combinations of skills: implications for cognitive architecture. Minds and Machines, 9 (2): 197-221.
54. Hadley, R. F., and Cardei, V. C., 1999, Language acquisition from sparse input without error feedback. Neural Networks, 12 (2): 217-235.
55. Hadley, R. F., and Hayward, M. B., 1997, Strong semantic systematicity from Hebbian connectionist learning. Minds and Machines, 7 (1): 1-37.
56. Hanson, S. J., and Kegl, J., 1987, PARSNIP: a connectionist network that learns natural language grammar from exposure to natural language sentences. In Program of the Ninth Annual Conference of the Cognitive Science Society, Seattle Washington (Hillsdale, NJ: Lawrence Erlbaum Associates), pp. 106-119.
57. Haselager, W. F. G., and van Rappard, J. F. H., 1998, Connectionism, systematicity, and the frame problem. Minds and Machines, 8 (2): 161-179.
58. Ho, K. S. E., and Chan, L. W., 1997, Confluent preorder parsing of deterministic grammars. Connection Science 9 (3): 269-293.
59. Ho, K. S. E., and Chan, L. W., 1999, How to design a connectionist holistic parser. Neural Computation, 11 (8): 1995-2016.
60. Morgan, T., and Tienson, J., 1996, Connectionism and the Philosophy of Psychology (Cambridge, MA: MIT Press).
61. Jeltsch, E., and Kreowski, H.-J., 1991, Grammatical inference based on hyperedge replacement. Lecture Notes in Computer Science, 532: 461-474.
62. Jordan, M. I., 1988, Serial Order: A Parallel Distributed Processing Approach, No. 8604. Institute of Cognitive Science, University of California, San Diego.
63. Kwasny, S. C., and Faisal, K. A., 1990, Connectionism and determinism in a syntactic parser. Connection Science, 2 (1, 2): 63-82.
64. Lang, K. J., 1992, Random DFA's can be approximately learned from sparse uniform examples. In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, Pittsburgh, PA (New York: Association for Computing Machinery), pp. 45-52.
65. Lari, K., and Young, S. J., 1990, The estimation of stochastic context-free grammars using the inside-outside algorithm. Computer Speech & Language, 4 (1): 35-56.
66. Levy, L. S., and Joshi, A. K., 1978, Skeletal structural descriptions. Information and Control, 39: 192-211.
67. Lichtblau, U., 1991, Recognizing rooted context-free flowgraph languages in polynomial time. Lecture Notes in Computer Science, 532: 538-548.
68. Lucas, S. M., 1993, New directions in grammatical inference. In Grammatical Inference: Theory, Applications and Alternatives, First Colloquium on Grammatical Inference, Essex, UK, 22-23 April 1993, IEE Digest No. 1993/092 (London: IEE).
69. Lucas, S. M., and Damper, R. I., 1990, Syntactic neural networks. Connection Science, 2: 195-221.
70. Main, M. G., and Rozenberg, G., 1987, Fundamentals of edge-label controlled graph grammars. Lecture Notes in Computer Science, 291: 411-426.
71. Mäkinen, E., 1994, On the relationship between diagram synthesis and grammatical inference. International Journal of Computer Mathematics, 52: 129-137.
72. Marcus, G. F., 1998, Rethinking eliminative connectionism. Cognitive Psychology, 37 (3): 243-282.
73. Mitchell, T. M., 1978, Version Spaces: An Approach to Concept Learning, PhD thesis, Stanford University.
74. Nagl, M., 1987, Set theoretic approaches to graph grammars. Lecture Notes in Computer Science, 291: 41-54.
75. Naumann, S., and Schrepp, J., 1993, Grammatical inference in DACS. In Grammatical Inference: Theory, Applications and Alternatives, First Colloquium on Grammatical Inference, Essex, UK, 22-23 April 1993, IEE Digest No. 1993/092 (London: IEE).
76. Newell, A., and Simon, H., 1976, Computer science as empirical inquiry: symbols and search. Communications of the Association for Computing Machinery, 19 (3): 113-126.
77. Phillips, S., 1999, Systematic minds, unsystematic models: learning transfer in humans and networks. Minds and Machines, 9 (3): 383-398.
78. Radhakrishnan, V., and Nagaraja, G., 1987, Inference of regular grammars via skeletons. IEEE Transactions on Systems, Man and Cybernetics, SMC-17 (6): 982-992.
79. Reilly, R., 1991, Connectionist technique for on-line parsing. Network, 3: 37-45.
80. Ron, D., Singer, Y., and Tishby, N., 1994, The power of amnesia. In J. Cowan, G. Tesauro and J. Alspector (eds) Advances in Neural Information Processing Systems 6 (San Mateo, CA: Morgan Kaufmann), p. 176.
81. Roques, M., 1994, Dynamic grammatical representation in guided propagation networks. Lecture Notes in Artificial Intelligence, 862: 189-202.
82. Rozenberg, G. (ed.), 1997, Handbook of Graph Grammars and Computing by Graph Transformation (Singapore: World Scientilic).
83. Ruiz, J., and García, P., 1996, Learning k-piecewise testable languages from positive data. Lecture Notes in Artificial Intelligence, 1147: 203-210.
84. Sakakibara, Y., 1997, Recent advances of grammatical inference. Theoretical Computer Science, 185 (1): 15-45.
85. Selman, B. A., 1985, Rule-based Processing in a Connectionist System for Natural Language Uunderstanding, CSRI-168, Computer Systems Research Institute, University of Toronto.
86. Smolensky, P., 1988, On the proper treatment of connectionism. Behavioral and Brain Sciences, 11 (1): 1-23.
87. Sougné, J., 1998, Connectionism and the problem of multiple instantiation. Trends in Cognitive Science, 2 (5): 183-189.
88. Stolcke, A., and Omohundro, S., 1994, Inducing probabilistic grammars by Bayesian model merging. Lecture Notes in Artificial Intelligence, 862: 106-118.
89. Tomita, M., 1982, Dynamic construction of finite automata from examples using hill-climbing. In Proceedings of the Fourth Annual Conference of the Cognitive Science Society, Ann Arbor Michigan (Boulder, CO: The Cognitive Science Society), pp. 105-108.
90. Tuller, A., 1967, A Modern Introduction to Geometries (Princeton, NJ: D. Van Nostrand Company).
91. Valiant, L. G., 1984, A theory of the learnable. Communications of the Association for Computing Machinery, 27 (11): 1134-1142.
92. Waltz, D. L., and Pollack, J. B., 1985, Massively parallel parsing: a strongly interactive model of natural-language interpretation. Cognitive Science, 9 (1): 51-74.
93. Williams, R. J., and Zipser, D., 1989, Experimental analysis of the real-time recurrent learning algorithm. Connection Science, 1 (1): 87-111.
94. Zeng, Z., Goodman, R. M., and Smyth, P., 1994, Discrete recurrent neural networks for grammatical inference. IEEE Transactions on Neural Networks, 5 (2): 320-330.