Generalized Predictive Shift-Reduce Parsing for Hyperedge Replacement Graph Grammars

Hoffmann, Berthold; Minas, Mark

doi:10.1007/978-3-030-13435-8_17

Cited by 7 publications

(17 citation statements)

References 10 publications

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“…Moreover, we reconsider the examples of series-parallel graphs and structured flowcharts, which we have used in [9]:…”

Section: Discussionmentioning

confidence: 99%

“…The following rules generate all graphs representing structured flowcharts: Z ε → begin x P xy end y P xy → S xy | P xz S zy S xy → instr xy | cond xuv P uy P vy | cond xuy P ux None of these grammars is PSR because their CFAs have conflicts. We used these examples in [9] to compare GPSR parsers with CYK parsers. We extend these experiments here and additionally compare these parsers with a GPSR parser using memoization.…”

Section: Discussionmentioning

confidence: 99%

“…We extend these experiments here and additionally compare these parsers with a GPSR parser using memoization. As in [9], we employ GPSR parsers with two different strategies for series-parallel graphs and for structured flowcharts. GPSR 1 employs a breadth-first strategy whereas GPSR 2 applies a more sophisticated strategy.…”

Section: Discussionmentioning

confidence: 99%

“…In [9] we have proposed generalized PSR (GPSR) parsing for grammars that are not PSR parseable. A GPSR parser is primarily a PSR parser that follows all different choices if a state has conflicts.…”

Section: Generalized Predictive Shift-reduce Parsingmentioning

confidence: 99%

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Speeding up Generalized PSR Parsers by Memoization Techniques

Minas¹

2019

Electron. Proc. Theor. Comput. Sci.

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Predictive shift-reduce (PSR) parsing for hyperedge replacement (HR) grammars is very efficient, but restricted to a subclass of unambiguous HR grammars. To overcome this restriction, we have recently extended PSR parsing to generalized PSR (GPSR) parsing along the lines of Tomita-style generalized LR parsing. Unfortunately, GPSR parsers turned out to be too inefficient without manual tuning. This paper proposes to use memoization techniques to speed up GPSR parsers without any need of manual tuning, and which has been realized within the graph parser distiller GRAPPA. We present running time measurements for some example languages; they show a significant speed up by some orders of magnitude when parsing valid graphs. But memoization techniques do not help when parsing invalid graphs or if all parses of an ambiguous input graph shall be determined.

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“…Moreover, we reconsider the examples of series-parallel graphs and structured flowcharts, which we have used in [9]:…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Generalized Predictive Shift-reduce Parsingmentioning

confidence: 99%

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Speeding up Generalized PSR Parsers by Memoization Techniques

Minas¹

2019

Electron. Proc. Theor. Comput. Sci.

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“…The proposed visualization and exploration approach can be used with many different types of visual parsers, either top-down or bottom-up. In fact, it has already been used with two different VGLR parser approaches based on [contextual] hyperedge replacement grammars ([C]HRGs) [13,9,24] and extended positional grammars [7,8].…”

Section: The Application Example: Vglr Parsing and Nlpmentioning

confidence: 99%

Visualizing Visual Parser Execution

Costagliola

Rosa

Minas

2020

International Conferences on Distributed Multimedia Systems

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Parsing of visual structures like diagrams and graphs is more complicated than parsing strings. This is so because visual structures are inherently more complex than strings, because visual grammars are more difficult to write than string grammars, and because the algorithms for parsing visual structures are usually more complicated than for parsing strings. The developer of a visual parser, therefore, needs more tool support than a developer of a string parser. In fact, developing and debugging a visual parser without proper visualization of the parsing process is very challenging. This paper describes a visualization approach that arose from this need. Its main focus is on the interaction of the developer with the visualization tool in order to explore the execution process of the parser. It has evolved from experiences with developing and debugging parsers by applying different visual parsing methods. In order to better describe it we introduce a concrete example.

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