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Recognition and learning of a class of context-sensitive languages described by augmented regular expressions

AuthorsAlquézar Mancho, Renato ; Sanfeliu, Alberto
KeywordsContext sensitive languages
Finite automata
Formal languages
Grammatical inference
Regular expressions
Syntactic pattern recognition
Pattern recognition
Issue Date1997
CitationPattern Recognition 30(1): 163-182 (1997)
AbstractIn this paper, a new formalism that permits to represent a non-trivial class of context-sensitive languages, the Augmented Regular Expressions (AREs), is introduced. AREs augment the expressive power of Regular Expressions (REs) by including a set of constraints that involve the number of instances in a string of the operands of the star operations of an RE. An efficient algorithm is given to recognize language strings by AREs. Also a general learning method to infer AREs from examples is presented, that consists of a regular grammatical inference step, a DFA to RE transformation, an RE parsing of the examples, and a constraint induction process.
Publisher version (URL)http://dx.doi.org/10.1016/S0031-3203(96)00056-8
Appears in Collections:(IRII) Artículos
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