How to design a connectionist holistic parser

Neural Computation

Volumn 11, Issue 8, 1999, Pages 1995-2016

  Ho, Edward Kei Shin ^a   Chan, Lai Wan ^b  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL INTELLIGENCE; HUMAN; LANGUAGE; THEORETICAL MODEL;

ARTIFICIAL INTELLIGENCE; HUMANS; LANGUAGE; MODELS, THEORETICAL;

EID: 0033570852     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976699300016061     Document Type: Article

References (33)

1. Aho, A. V., Sethi, R., & Ullman, J. D. (1986). Compilers: Principles, techniques, and tools. Reading, MA: Addison-Wesley.
2. Allen, J. (1995). Natural language understanding. Redwood City, CA: Benjamin/Cummings.
3. Berg, G. (1992). A connectionist parser with recursive sentence structure and lexical disambiguation. Proc. of the Tenth National Conference on Artificial Intelligence (AAAI-92), San Jose (pp. 32-37).
4. Blank, D. S., Meeden, L. A., & Marshall, J. B. (1992). Exploring the symbolic/subsymbolic continuum: A case study of RAAM. In J. Dinsmore (Ed.), The symbolic and connectionist paradigms: Closing the gap (pp. 113-148). Hillsdale, NJ: Erlbaum.
5. Callan, R. E., & Palmer-Brown, D. (1997). (S)RAAM: An analytical technique for fast and reliable derivation of connectionist symbol structure representations. Connection Science, 9, 139-159.
6. Carroll, D. W. (1994). Psychology of language. Pacific Grove, CA: Brooks/Cole.
7. Chalmers, D. J. (1992). Syntactic transformation of distributed representations. In N. Sharkey (Ed.), Connectionist natural language processing (pp. 46-55). Boston: Kluwer.
8. Charniak, E. (1993). Statistical language learning. Cambridge, MA: MIT Press.
9. Chrisman, L. (1991). Learning recursive distributed representations for holistic computation. Connection Science, 3, 345-366.
10. Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179-211.
11. Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3-71.
12. Franz, A. (1996). Learning PP attachment from corpus statistics. In S. Wermter, E. Riloff, & G. Scheler (Eds.), Connectionist, statistical, and symbolic approaches to learning for natural language processing (pp. 188-202). Berlin: Springer-Verlag.
13. Gazdar, G., & Mellish, C. (1989). Natural Language Processing in Prolog: An introduction to computational linguistics. Reading, MA: Addison-Wesley.
14. Hammerton, J. A. (1998). Holistic computation: Reconstructing a muddled concept. Connection Science, 10, 3-9.
15. Ho, K. S. E., & Chan, L. W. (1994). Representing sentence structures in neural networks. Proc. of the International Conference in Neural Information Processing Systems 3, Seoul (pp. 1462-1467).
16. Ho, K. S. E., & Chan, L. W. (1997). Confluent preorder parsing of deterministic grammars. Connection Science, 9, 269-293.
17. Ho, K. S. E., & Wong, K. F. (1998). A neural network-based LR parser. Proc. the 1st International Symposium on Intelligent Data Engineering and Learning (IDEAL'98), Hong Kong (pp. 289-294).
18. Jain, A. N. (1991). Parsing complex sentences with structured connectionist networks. Neural Computation, 3, 110-120.
19. Krulee, G. K. (1991). Computer processing of natural language. Englewood Cliffs, NJ: Prentice Hall.
20. Kwasny, S. C., & Faisal, K. A. (1992). Symbolic parsing via subsymbolic rules. In J. Dinsmore (Ed.), The symbolic and connectionist paradigm: Closing the gap (pp. 209-236). Hillsdale, NJ: Erlbaum.
21. Kwasny, S. C., & Kalman, B. L. (1995). Tail-recursive distributed representations and simple recurrent networks. Connection Science, 7, 61-80.
22. Lin, T., Horne, B. G., Tino, P., & Giles, C. L. (1996). Learning long-term dependencies in NARX recurrent neural networks. IEEE Transactions on Neural Networks, 7, 1329-1338.
23. Marcus, M. P. (1980). A theory of syntactic recognition for natural language. Cambridge, MA: MIT Press.
24. Maskara, A., & Noetzel, A. (1993). Forced simple recurrent neural network and grammatical inference. Proc. of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 420-425).
25. Miikkulainen, R. (1996). Subsymbolic case-role analysis of sentences with embedded clauses. Cognitive Science, 20, 47-73.
26. Pollack, J. B. (1990). Recursive distributed representations. Artificial Intelligence, 46, 77-105.
27. Pollack, J. B., & Waltz, D. (1985). Massively parallel parsing: A strongly interative model of natural language interpretation. Cognitive Science, 9, 51-74.
28. Pollard, C., & Sag, I. (1993). Head-driven phase structure grammar. Chicago: University of Chicago Press.
29. Reilly, R. (1992). Connectionist techniques for on-line parsing. Network, 3, 37-45.
30. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations through error propagation. In D. E. Rumelhart, J. L. McClelland, & the PDP Research Group (Eds.), Parallel distributed processing: Experiments in the microstructure of cognition (pp. 318-362). Cambridge, MA: MIT Press.
31. Sharkey, N. E., & Sharkey, A. J. C. (1992). A modular design for connectionist parsing. Proc. of the Twente Workshop on Language Technology 3: Connectionism and Natural Language Processing (pp. 87-96).
32. Stolcke, A., & Wu, D. (1992). Tree matching with recursive distributed representations. (Tech. Rep. No. TR-92-025). Berkeley: International Computer Science Institute.
33. Sun, G. Z., Giles, C. L., Chen, H. H., & Lee, Y. C. (1993). The neural network pushdown automata: Model, stack and learning simulations (Tech. Rep. Nos. UMIACS-TR-93-77 and CS-TR-3118). College Park: University of Maryland.