Efficient recognition of a class of context-sensitive languages described by Augmented Regular Expressions

  • Alberto Sanfeliu
  • René Alquézar
Grammars and Languages
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1121)


Recently, Augmented Regular Expressions (AREs) have been proposed as a formalism to describe, recognize and learn a nontrivial 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 efficient 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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Alberto Sanfeliu
    • 1
  • René Alquézar
    • 2
  1. 1.Institut de Robòtica i Informàtica IndustrialUPC-CSICBarcelonaSpain
  2. 2.Dept. LSIUniversitat Politècnica de CatalunyaBarcelona

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