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Generalized Predictive Shift-Reduce Parsing for Hyperedge Replacement Graph Grammars

  • Berthold Hoffmann
  • Mark MinasEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11417)

Abstract

Parsing for graph grammars based on hyperedge replacement (HR) is in general NP-hard, even for a particular grammar. The recently developed predictive shift-reduce (PSR) parsing is efficient, but restricted to a subclass of unambiguous HR grammars. We have implemented a generalized PSR parsing algorithm that applies to all HR grammars, and pursues severals parses in parallel whenever decision conflicts occur. We compare GPSR parsers with the Cocke-Younger-Kasami parser and show that a GPSR parser, despite its exponential worst-case complexity, can be much faster.

Keywords

Hyperedge replacement grammar Graph parsing 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universität BremenBremenGermany
  2. 2.Universität der Bundeswehr MünchenNeubibergGermany

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