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ContextFree Language Induction by
"... The process of learning often consists of Inductive Inference, making generalizations from samples. The problem here is finding generalizations (Grammars) for Formal Languages from finite sets of positive and negative sample sentences. The focus of this paper is on ContextFree Languages (CFL&a ..."
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The process of learning often consists of Inductive Inference, making generalizations from samples. The problem here is finding generalizations (Grammars) for Formal Languages from finite sets of positive and negative sample sentences. The focus of this paper is on ContextFree Languages (CFL
Universal coalgebra: a theory of systems
, 2000
"... In the semantics of programming, nite data types such as finite lists, have traditionally been modelled by initial algebras. Later final coalgebras were used in order to deal with in finite data types. Coalgebras, which are the dual of algebras, turned out to be suited, moreover, as models for certa ..."
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Cited by 408 (42 self)
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for certain types of automata and more generally, for (transition and dynamical) systems. An important property of initial algebras is that they satisfy the familiar principle of induction. Such a principle was missing for coalgebras until the work of Aczel (NonWellFounded sets, CSLI Leethre Notes, Vol. 14
Inductive Inference of Contextfree Languages
"... An inductive inference problem of contextfree languages is investigated. There have been many attempts to this problem, and most of them are based on a problem setting in which a representation space for hypotheses is a class of contextfree grammars. An inference algorithm given in this paper, on ..."
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An inductive inference problem of contextfree languages is investigated. There have been many attempts to this problem, and most of them are based on a problem setting in which a representation space for hypotheses is a class of contextfree grammars. An inference algorithm given in this paper
Inductive Bias in ContextFree Language Learning
 IN PROCEEDINGS OF THE NINTH AUSTRALIAN CONFERENCE ON NEURAL NETWORKS
, 1998
"... Recurrent neural networks are capable of learning contextfree tasks, however learning performance is unsatisfactory. We investigate the effect of biasing learning towards finding a solution to a contextfree prediction task. The first series of simulations #xes various sets of weights of the net ..."
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Cited by 20 (10 self)
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Recurrent neural networks are capable of learning contextfree tasks, however learning performance is unsatisfactory. We investigate the effect of biasing learning towards finding a solution to a contextfree prediction task. The first series of simulations #xes various sets of weights
A version space approach to learning contextfree grammars
 Machine Learning
, 1987
"... learning from examples. Abstract. In principle, the version space approach can be applied to any induction problem. However, in some cases the representation language for generalizations is so powerful that (1) some of the update functions for the version space are not effectively computable, and (2 ..."
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Cited by 35 (0 self)
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learning from examples. Abstract. In principle, the version space approach can be applied to any induction problem. However, in some cases the representation language for generalizations is so powerful that (1) some of the update functions for the version space are not effectively computable
Evolving Stochastic ContextFree Grammars from Examples Using a Minimum Description Length Principle
 Paper presented at the Workshop on Automata Induction Grammatical Inference and Language Acquisition, ICML97
"... This paper describes an evolutionary approach to the problem of inferring stochastic contextfree grammars from finite language samples. The approach employs a genetic algorithm, with a fitness function derived from a minimum description length principle. Solutions to the inference problem are evolv ..."
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Cited by 21 (0 self)
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This paper describes an evolutionary approach to the problem of inferring stochastic contextfree grammars from finite language samples. The approach employs a genetic algorithm, with a fitness function derived from a minimum description length principle. Solutions to the inference problem
Contextfree languages via coalgebraic trace semantics
 International Conference on Algebra and Coalgebra in Computer Science (CALCOâ€™05), volume 3629 of Lect. Notes Comp. Sci
, 2005
"... Abstract. We show that, for functors with suitable mild restrictions, the initial algebra in the category of sets and functions gives rise to the final coalgebra in the (Kleisli) category of sets and relations. The finality principle thus obtained leads to the finite trace semantics of nondeterminis ..."
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Cited by 18 (8 self)
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of nondeterministic systems, which extends the trace semantics for coalgebras previously introduced by the second author. We demonstrate the use of our technical result by giving the first coalgebraic account on contextfree grammars, where we obtain generated contextfree languages via the finite trace semantics
Contextfree Grammar Induction using Genetic Programming
"... While grammar inference is used in areas like natural language acquisition, syntactic pattern recognition, etc., its application to the programming language problem domain has been limited. We propose a new application area for grammar induction which intends to make domainspecific language develop ..."
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in inducing small grammars. We extend that work and propose the use of derivation trees and syntax graphs in order to be able to infer a more comprehensive set of contextfree grammars.
Learning ContextFree Grammars with a Simplicity Bias
 Proceedings of the Eleventh European Conference on Machine Learning
, 2000
"... . We examine the role of simplicity in directing the induction of contextfree grammars from sample sentences. We present a rational reconstruction of Wolff's SNPR  the Grids system  which incorporates a bias toward grammars that minimize description length. The algorithm alternates bet ..."
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Cited by 46 (4 self)
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on the task of inducing contextfree grammars from training sentences. Much recent work on this topic has dealt with learning finitestate structures, but there is considerable evidence that human language involves more powerful grammatical representations. In contextfree grammar induction, the learner must
TECHNIQUES FOR CONTEXTFREE GRAMMAR INDUCTION AND APPLICATIONS
, 2007
"... Grammar Inference is the process of learning a grammar from examples, either positive (i.e., the grammar generates the string) and/or negative (i.e., the grammar does not generate the string). Although grammar inference has been successfully applied to many diverse domains such as speech recognitio ..."
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recognition and robotics, its application to software engineering has been limited. This research investigates the applicability of grammar inference to software engineering and programming language development challenge problems, where grammar inference offers an innovative solution to the problem, while
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