Predictive Shift-Reduce Parsing for Hyperedge Replacement Grammars
نویسندگان
چکیده
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.
منابع مشابه
Generating Efficient Predictive Shift-Reduce Parsers for Hyperedge Replacement Grammars
Predictive shift-reduce (PSR) parsing for a subclass of hyperedge replacement graph grammars has recently been devised by Frank Drewes and the authors [6]. This paper describes in detail how efficient PSR parsers are generated with the Grappa parser generator implemented by Mark Minas. Measurements confirm that the generated parsers run in linear time.
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