Grammar-based connectionist approaches to language
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[1] Michael C. Mozer,et al. Perception of multiple objects - a connectionist approach , 1991, Neural network modeling and connectionism.
[2] Michael C. Mozer,et al. Mathematical Perspectives on Neural Networks , 1996 .
[3] R. Langacker. Foundations of cognitive grammar , 1983 .
[4] James L. McClelland. Toward a theory of information processing in graded, random, and interactive networks , 1993 .
[5] J J Hopfield,et al. Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.
[6] L. Shastri,et al. From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony , 1993, Behavioral and Brain Sciences.
[7] Alan Prince,et al. Prosodic morphology : constraint interaction and satisfaction , 1993 .
[8] Caroline Heycock,et al. Language Acquisition: Knowledge Representation and Processing , 1999 .
[9] I. Biederman,et al. Dynamic binding in a neural network for shape recognition. , 1992, Psychological review.
[10] Cheryl Cydney Zoll,et al. Parsing Below the Segment in a Constraint-Based Framework , 1998 .
[11] A Prince,et al. Optimality: From Neural Networks to Universal Grammar , 1997, Science.
[12] G. O. Stone,et al. An analysis of the delta rule and the learning of statistical associations , 1986 .
[13] George Lakoff,et al. Women, Fire, and Dangerous Things , 1987 .
[14] Geoffrey E. Hinton,et al. Learning distributed representations of concepts. , 1989 .
[15] Yves Chauvin,et al. Backpropagation: the basic theory , 1995 .
[16] P. Smolensky. On the proper treatment of connectionism , 1988, Behavioral and Brain Sciences.
[17] Y. Miyata,et al. Harmonic grammar: A formal multi-level connectionist theory of linguistic well-formedness: Theoretic , 1990 .
[18] Géraldine Legendre,et al. Principles for an Integrated Connectionist/Symbolic Theory of Higher Cognition ; CU-CS-600-92 , 1992 .
[19] David S. Touretzky,et al. A Computational Basis for Phonology , 1989, NIPS.
[20] David Zipser,et al. Feature Discovery by Competive Learning , 1986, Cogn. Sci..
[21] Teuvo Kohonen,et al. Associative memory. A system-theoretical approach , 1977 .
[22] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .
[23] Geoffrey E. Hinton,et al. OPTIMAL PERCEPTUAL INFERENCE , 1983 .
[24] Geoffrey E. Hinton,et al. A Distributed Connectionist Production System , 1988, Cogn. Sci..
[25] Nick Chater,et al. Connectionist natural language processing: the state of the art , 1999 .
[26] John J. Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities , 1999 .
[27] P. Smolensky,et al. When is less more? Faithfulness and minimal links in wh-chains , 1998 .
[28] Katya Zubritskaya,et al. Mechanism of sound change in Optimality Theory , 1997, Language Variation and Change.
[29] Tony Plate,et al. Holographic Reduced Representations: Convolution Algebra for Compositional Distributed Representations , 1991, IJCAI.
[30] Stephen A. Ritz,et al. Distinctive features, categorical perception, and probability learning: some applications of a neural model , 1977 .
[31] S. Pinker,et al. On language and connectionism: Analysis of a parallel distributed processing model of language acquisition , 1988, Cognition.
[32] T. Bever,et al. The relation between linguistic structure and associative theories of language learning—A constructive critique of some connectionist learning models , 1988, Cognition.
[33] M. McCloskey. Networks and Theories: The Place of Connectionism in Cognitive Science , 1991 .
[34] D. Gary Miller,et al. 1. Theoretical Prerequisites , 1994 .
[35] Paul Smolensky,et al. Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1990, Artif. Intell..
[36] W. Freeman. Second Commentary: On the proper treatment of connectionism by Paul Smolensky (1988) - Neuromachismo Rekindled , 1989 .
[37] Bruce Tesar,et al. Learning optimality-theoretic grammars☆ , 1998 .
[38] Michael C. Mozer,et al. Rule Induction through Integrated Symbolic and Subsymbolic Processing , 1991, NIPS.
[39] Jordan B. Pollack,et al. Recursive Distributed Representations , 1990, Artif. Intell..
[40] James L. McClelland,et al. On learning the past-tenses of English verbs: implicit rules or parallel distributed processing , 1986 .
[41] Leonard Talmy,et al. Force Dynamics in Language and Cognition , 1987, Cogn. Sci..
[42] C. P. Dolan,et al. Tensor manipulation networks: connectionist and symbolic approaches to comprehension, learning, and planning , 1989 .
[43] P. Smolensky,et al. Harmonic Grammar -- A Formal Multi-Level Connectionist Theory of Linguistic Well-Formedness: An Application ; CU-CS-464-90 , 1990 .
[44] J. Blevins. The Syllable in Phonological Theory , 1995 .
[46] P. Smolensky. On the comprehension/production dilemma in child language , 1996 .
[47] Mitsuhiko Ota,et al. Optimality Theory: an overview , 2000 .
[48] Terrence J. Sejnowski,et al. Parallel Networks that Learn to Pronounce English Text , 1987, Complex Syst..
[49] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[50] Michael I. Jordan. Attractor dynamics and parallelism in a connectionist sequential machine , 1990 .
[51] Jerome A. Feldman,et al. Connectionist Models and Their Properties , 1982, Cogn. Sci..
[52] Paul Smolensky,et al. Lexical and postlexical processes in spoken word production , 1999 .
[53] Geoffrey E. Hinton,et al. Learning and relearning in Boltzmann machines , 1986 .
[54] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[55] Pilar Barbosa,et al. Is the best good enough? : optimality and competition in syntax , 1998 .
[56] James L. McClelland,et al. A distributed, developmental model of word recognition and naming. , 1989, Psychological review.
[57] T. Shallice,et al. Connectionist Modelling in Cognitive Neuropsychology: A Case Study , 1994 .
[58] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[59] S. Pinker,et al. Connections and symbols , 1988 .
[60] R. Jakobson. Child Language, Aphasia and Phonological Universals , 1980 .
[61] GrossbergS.. Adaptive pattern classification and universal recoding , 1976 .
[62] R. Jakobson,et al. Selected Writings: I. Phonological Studies , 1965 .
[63] Paul Smolensky,et al. Schema Selection and Stochastic Inference in Modular Environments , 1983, AAAI.