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Formalization and Correctness of Predictive Shift-Reduce Parsers for Graph Grammars Based on Hyperedge Replacement
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)ORCID iD: 0000-0001-7349-7693
University of Bremen.
Universität der Bundeswehr München.
2019 (English)In: The Journal of logical and algebraic methods in programming, ISSN 2352-2208, E-ISSN 2352-2216, Vol. 104, p. 303-341Article in journal (Refereed) Published
Abstract [en]

Hyperedge replacement (HR) grammars can generate NP-complete graph languages, which makes parsing hard even for fixed HR languages. Therefore, we study predictive shift-reduce (PSR) parsing that yields efficient parsers for a subclass of HR grammars, by generalizing the concepts of SLR(1) string parsing to graphs. We formalize the construction of PSR parsers and show that it is correct. PSR parsers run in linear space and time, and are more efficient than the predictive top-down (PTD) parsers recently developed by the authors.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 104, p. 303-341
Keywords [en]
hyperedge replacement grammar, graph parsing, grammar analysis
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-155799DOI: 10.1016/j.jlamp.2018.12.006OAI: oai:DiVA.org:umu-155799DiVA, id: diva2:1283102
Available from: 2019-01-28 Created: 2019-01-28 Last updated: 2019-04-15Bibliographically approved

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Drewes, Frank

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