Efficient Recognition of a Class of Context-Sensitive Languages Described by Augmented Regular Expressions
نویسندگان
چکیده
Recently, Augmented Regular Expressions (AREs) have been proposed as a formalism to describe, recognize and learn a non-trivial class of context-sensitive languages (CSLs) 1, 2]. 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. Although it is demonstrated that not all the CSLs can be described by AREs, the class of representable objects includes planar shapes with symmetries, which is important for pattern recognition tasks. Likewise, it is proved that AREs cover all the pattern languages 3]. An eecient algorithm is presented to recognize language strings by means of AREs. The method is splitted in two stages: parsing the string by the underlying regular expression and checking that the resulting star instances satisfy the constraints.
منابع مشابه
Learning of context-sensitive languages described by augmented regular expressions
Recently, Augmented Regular Expressions (AREs) have been proposed as a formalism to describe and recognize a non-trivial class of context-sensitive languages (CSLs) [1, 2]. 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 a RE. Although it has been demonstr...
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