Approximating Parikh Images for Generating Deterministic Graph Parsers

Drewes, Frank; Hoffmann, Berthold; Minas, Mark

doi:10.1007/978-3-319-50230-4_9

Cited by 6 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Before parsing starts, a procedure described in [4,Sect. 4] analyzes the grammar for the unique start node property, by computing the possible incidences of all nodes created by a grammar.…”

Section: Example 1 (A Hr Grammar For Sierpinski Triangles)mentioning

confidence: 99%

“…The node x in the start rule Z ε → D xyz must be bound to the topmost node of any input graph. 4 If the input graph has no topmost node, or more than one, it cannot be a Sierpinski graph, so that parsing fails immediately. A PSR parser is a push-down automaton that is controlled by a characteristic finite automaton (CFA).…”

Section: Example 1 (A Hr Grammar For Sierpinski Triangles)mentioning

confidence: 99%

See 1 more Smart Citation

Speeding up Generalized PSR Parsers by Memoization Techniques

Minas¹

2019

Electron. Proc. Theor. Comput. Sci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Example 1 (A Hr Grammar For Sierpinski Triangles)mentioning

confidence: 99%

Section: Example 1 (A Hr Grammar For Sierpinski Triangles)mentioning

confidence: 99%

Speeding up Generalized PSR Parsers by Memoization Techniques

Minas¹

2019

Electron. Proc. Theor. Comput. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since even a cubic algorithm would not scale to diagrams occurring in realistic applications, the authors have recently transferred results of context-free string parsing to graphs: Simple LL-parsing (SLL(k) for short, [28]), a top-down parsing method that applies to a subclass of unambiguous context-free string grammars (using k symbols of lookahead), has been lifted to predictive top-down parsing of graphs (PTD parsing for short, [12]); the program generating PTD parsers approximates Parikh images of auxiliary grammars in order to determine whether a grammar is PTD-parsable [13], and generates parsers that run in quadratic time, and in many cases in linear time.…”

Section: Introductionmentioning

confidence: 99%

Formalization and correctness of predictive shift-reduce parsers for graph grammars based on hyperedge replacement

Drewes¹,

Hoffmann²,

Minas³

2019

Journal of Logical and Algebraic Methods in Programming

Self Cite

View full text Add to dashboard Cite

Hyperedge replacement (HR) grammars can generate NP-complete graph languages, which makes parsing hard even for fixed HR languages. Therefore, we study predictive shift-reduce (PSR) parsing that yields efficient parsers for a subclass of HR grammars, by generalizing the concepts of SLR(1) string parsing to graphs. We formalize the construction of PSR parsers and show that it is correct. PSR parsers run in linear space and time, and are more efficient than the predictive top-down (PTD) parsers recently developed by the authors. ✩ This paper formalizes the concepts described in [14] and provides detailed correctness proofs for them.

show abstract

Generalized Predictive Shift-Reduce Parsing for Hyperedge Replacement Graph Grammars

Hoffmann

Minas

2019

Language and Automata Theory and Applications

View full text Add to dashboard Cite

Approximating Parikh Images for Generating Deterministic Graph Parsers

Cited by 6 publications

References 12 publications

Speeding up Generalized PSR Parsers by Memoization Techniques

Speeding up Generalized PSR Parsers by Memoization Techniques

Formalization and correctness of predictive shift-reduce parsers for graph grammars based on hyperedge replacement

Generalized Predictive Shift-Reduce Parsing for Hyperedge Replacement Graph Grammars

Contact Info

Product

Resources

About