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Predictive Shift-Reduce Parsing for Hyperedge Replacement Grammars

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Graph Transformation (ICGT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10373))

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Abstract

Graph languages defined by hyperedge replacement grammars can be NP-complete. We study predictive shift-reduce (PSR) parsing for a subclass of these grammars, which generalizes the concepts of SLR(1) string parsing to graphs. PSR parsers run in linear space and time. In comparison to the predictive top-down (PTD) parsers recently developed by the authors, PSR parsing is more efficient and more general, while the required grammar analysis is easier than for PTD parsing.

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Notes

  1. 1.

    This result has been exploited for parsing natural language in the system Bolinas [2].

  2. 2.

    We assume that this order is provided with the HR grammar. Finding an appropriate order for PSR parsing automatically can be done by dataflow analysis, but is outside the scope of this paper.

  3. 3.

    In general, we may have to introduce fresh names for non-parameter nodes in the closure items as well in order to avoid name clashes, but this is not necessary in the present example.

  4. 4.

    The Grappa tool is available at www.unibw.de/inf2/grappa; the examples mentioned in Table 1 can be found there as well.

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Author information

Authors and Affiliations

  1. Umeå universitet, Umeå, Sweden

    Frank Drewes

  2. Universität Bremen, Bremen, Germany

    Berthold Hoffmann

  3. Universität der Bundeswehr München, Neubiberg, Germany

    Mark Minas

Authors
  1. Frank Drewes
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  2. Berthold Hoffmann
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  3. Mark Minas
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Correspondence to Mark Minas .

Editor information

Editors and Affiliations

  1. Universidad Autónoma de Madrid, Madrid, Spain

    Juan de Lara

  2. University of York, York, United Kingdom

    Detlef Plump

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Drewes, F., Hoffmann, B., Minas, M. (2017). Predictive Shift-Reduce Parsing for Hyperedge Replacement Grammars. In: de Lara, J., Plump, D. (eds) Graph Transformation. ICGT 2017. Lecture Notes in Computer Science(), vol 10373. Springer, Cham. https://doi.org/10.1007/978-3-319-61470-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-61470-0_7

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