Introduction to automata theory, languages, and computation, 2nd edition

Hopcroft, John E.; Motwani, Rajeev; Ullman, Jeffrey D.

doi:10.1145/568438.568455

Cited by 1,891 publications

(2,732 citation statements)

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“…He has shown that left-associative grammar can be expressed as a finite-state grammar that orders words in terms of part-of-speech categories. Because we know that finitestate grammars can be acquired from positive evidence (Hopcroft & Ullman, 1979), this means that children should be able to learn left-associative grammars directly without triggering a logical problem. Given the fact that these grammars can parse sentences in a time-linear and psycholinguistically plausible fashion, they would seem to be excellent candidates for further exploration by child language researchers.…”

Section: Limiting the Class Of Grammarsmentioning

confidence: 99%

A multiple process solution to the logical problem of language acquisition

2004

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Many researchers believe that there is a logical problem at the center of language acquisition theory. According to this analysis, the input to the learner is too inconsistent and incomplete to determine the acquisition of grammar. Moreover, when corrective feedback is provided, children tend to ignore it. As a result, language learning must rely on additional constraints from universal grammar. To solve this logical problem, theorists have proposed a series of constraints and parameterizations on the form of universal grammar. Plausible alternatives to these constraints include: conservatism, item-based learning, indirect negative evidence, competition, cue construction, and monitoring. Careful analysis of child language corpora has cast doubt on claims regarding the absence of positive exemplars. Using demonstrably available positive data, simple learning procedures can be formulated for each of the syntactic structures that have traditionally motivated invocation of the logical problem. Within the perspective of emergentist theory (MacWhinney, 2001), the operation of a set of mutually supportive processes is viewed as providing multiple buffering for developmental outcomes. However, the fact that some syntactic structures are more difficult to learn than others can be used to highlight areas of intense grammatical competition and processing load.

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Section: Limiting the Class Of Grammarsmentioning

confidence: 99%

A multiple process solution to the logical problem of language acquisition

2004

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“…The performance of our network naturally depends on the depth of the stack, and as such it can be replaced by a finite-state automaton (Hopcroft and Ullman, 1979). However, in this sense human parsing ability is also limited: no person can parse sentences with arbitrarily many levels of embeddings (Pinker, 1994).…”

Section: Resultsmentioning

confidence: 99%

“…It is well known that parsing of CER can be solved very efficiently by a stack (push-down automaton), with the necessary pop and push operations (Hopcroft and Ullman, 1979). Thus it would be a step forward to present a neurally plausible simple stack architecture that could parse CER.…”

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confidence: 99%

Parsing recursive sentences with a connectionist model including a neural stack and synaptic gating

Fedor¹,

Ittzés²,

Szathmáry³

2011

Journal of Theoretical Biology

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To cite this version:Anna Fedor, Péter Péter Ittzés, Eörs Eörs Szathmáry. Parsing recursive sentences with a connectionist model including a neural stack and synaptic gating. Journal of Theoretical Biology, Elsevier, 2011, 271 (1) This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractIt is supposed that humans are genetically predisposed to be able to recognize sequences of context free grammars with center-embedded recursion while other primates are restricted to the recognition of finite state grammars with tail-recursion. Our aim was to construct a minimalist neural network that is able to parse artificial sentences of both grammars in an efficient way without using the biologically unrealistic backpropagation algorithm. The core of this network is a neural stack-like memory where the push and pop operations are regulated by synaptic gating on the connections between the layers of the stack. The network correctly categorizes novel sentences of both grammars after training. We suggest that the introduction of the neural stack memory will turn out to be substantial for any biological 'hierarchical processor' and the minimalist design of the model suggests a quest for similar, realistic neural architectures.

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“…Since the heuristic heuristic transformation rules used in the network construction preserve flask stoichiometry (Scheme 1), the nodes of the the resulting network representation correspond to the possible states of the reactive system with fixed stoichiometry. We refer to this network representation as the finite-state representation, by analogy with finite-state machines, 61 and contrast it with the commonly used representations of metabolic networks as interaction networks, in which individual metabolites are network nodes and network edges connect all reaction participants with each other. 5,32,62,63 The finite-state representation of the reaction network is a directed network with edge weights given by thermodynamic and / or kinetic parameter values.…”

Section: Probing the Chemistry Of The Formose Reaction Networkmentioning

confidence: 99%

Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry

Rappoport

Galvin

Zubarev

et al. 2014

J. Chem. Theory Comput.

127

155

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While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here we present heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reactive potential energy surfaces and are combined here with quantum chemical structure optimizations, which yield the structures and energies of the reaction intermediates and products. Application of heuristics-aided quantum chemical methodology to the formose reaction of prebiotic evolution reproduces the experimentally observed reaction products, major reaction pathways, and autocatalytic cycles.

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Introduction to automata theory, languages, and computation, 2nd edition

Cited by 1,891 publications

References 0 publications

A multiple process solution to the logical problem of language acquisition

A multiple process solution to the logical problem of language acquisition

Parsing recursive sentences with a connectionist model including a neural stack and synaptic gating

Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry

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