Extending Predictive Shift-Reduce Parsing to Contextual Hyperedge Replacement Grammars

Drewes, Frank; Hoffmann, Berthold; Minas, Mark

doi:10.1007/978-3-030-23611-3_4

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…We also mention the body of work by Drewes, Hoffmann, and Minas on parsing algorithms for HRGs and contextual HRGs (a stronger formalism); see, for example, [23,21,22]. Parsing for these formalisms is NP-complete in general, but the authors provide a procedure that given a contextual HRG generates a parser for the language produced by the grammar.…”

Section: Related Workmentioning

confidence: 99%

Transduction from Trees to Graphs Through Folding

Berglund

Björklund

et al. 2022

SSRN Journal

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Section: Related Workmentioning

confidence: 99%

Transduction from Trees to Graphs Through Folding

Berglund

Björklund

et al. 2022

SSRN Journal

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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%

“…The authors have realized several visual parsers and visual parser generator [7,8,9,13,24]. Developing VGLR parsers is challenging because one has to coordinate several non-trivial data structures like the input, the GSS, and the parse forest.…”

Section: Use Casesmentioning

confidence: 99%

Visualizing Visual Parser Execution

Costagliola

Rosa

Minas

2020

International Conferences on Distributed Multimedia Systems

Self Cite

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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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“…We have previously shown that contextual hyperedge-replacement grammars (CHRGs), an extension of the well-known context-free hyperedge-replacement grammars (HRGs), can handle both structural and non-structural reentrancies [22]. Moreover, a parser generator has been provided by Drewes, Hoffmann, and Minas for CHRGs that, when it succeeds in producing a parser, guarantees that the parser will run in quadratic (and in the common case, linear) time in the size of its input graph [19], [21]. However, similarly to common LL-and LR-parsers for context-free languages the parser generator may discover a parsing conflict, thus failing to output a parser.…”

Section: Introductionmentioning

confidence: 99%

Polynomial Graph Parsing with Non-Structural Reentrancies

Björklund,

Drewes,

Jonsson

2021

Preprint

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Graph-based semantic representations are valuable in natural language processing, where it is often simple and effective to represent linguistic concepts as nodes, and relations as edges between them. Several attempts has been made to find a generative device that is sufficiently powerful to represent languages of semantic graphs, while at the same allowing efficient parsing. We add to this line of work by introducing graph extension grammar, which consists of an algebra over graphs together with a regular tree grammar that generates expressions over the operations of the algebra. Due to the design of the operations, these grammars can generate graphs with non-structural reentrancies, a type of node-sharing that is excessively common in formalisms such as abstract meaning representation, but for which existing devices offer little support. We provide a parsing algorithm for graph extension grammars, which is proved to be correct and run in polynomial time.

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Extending Predictive Shift-Reduce Parsing to Contextual Hyperedge Replacement Grammars

Cited by 10 publications

References 16 publications

Transduction from Trees to Graphs Through Folding

Transduction from Trees to Graphs Through Folding

Visualizing Visual Parser Execution

Polynomial Graph Parsing with Non-Structural Reentrancies

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