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.
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Notes
- 1.
In the graph parser generator Grappa, available at www.unibw.de/inf2/grappa.
- 2.
\(V_{\!\gamma }\) may contain isolated nodes that are not attached to any edge in \(\gamma \).
- 3.
I.e., a match \(\mu \) makes sure that the nodes of \(\alpha ^\mu \) that do not occur in \(\bar{A} = A^\mu \) do not collide with the other nodes in \(\gamma \).
- 4.
We silently assume that input graphs do not have isolated nodes. This is no real restriction as one can add special edges to such nodes.
- 5.
Actually, series-parallel graphs do also have a unique sink (without outgoing edges), which could be used as a second start node bound to y. However, this variation of the grammar would exhibit less peculiarities of the GPSR parser.
- 6.
This property can be determined by the parser generator as well. However, it does not hold for the grammar of series-parallel graphs.
- 7.
Homepage: www.unibw.de/inf2/diagen.
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Hoffmann, B., Minas, M. (2019). Generalized Predictive Shift-Reduce Parsing for Hyperedge Replacement Graph Grammars. In: Martín-Vide, C., Okhotin, A., Shapira, D. (eds) Language and Automata Theory and Applications. LATA 2019. Lecture Notes in Computer Science(), vol 11417. Springer, Cham. https://doi.org/10.1007/978-3-030-13435-8_17
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