Parsing Graphs with Regular Graph Grammars

Gilroy, Sorcha; Lopez, Adam; Maneth, Sebastian

doi:10.18653/v1/s17-1024

Cited by 11 publications

(6 citation statements)

References 13 publications

(14 reference statements)

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“…This result is comparable to another algorithm proposed by Gilroy et al (2017). However, the strong restrictions of RGG make it too weak to model linguistic structures.…”

Section: Weakly Regular Graph Grammarsupporting

confidence: 67%

Exact yet Efficient Graph Parsing, Bi-directional Locality and the Constructivist Hypothesis

Ye¹,

Sun

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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A key problem in processing graph-based meaning representations is graph parsing, i.e. computing all possible derivations of a given graph according to a (competence) grammar. We demonstrate, for the first time, that exact graph parsing can be efficient for large graphs and with large Hyperedge Replacement Grammars (HRGs). The advance is achieved by exploiting locality as terminal edge-adjacency in HRG rules. In particular, we highlight the importance of 1) a terminal edge-first parsing strategy, 2) a categorization of a subclass of HRG, i.e. what we call Weakly Regular Graph Grammar, and 3) distributing argumentstructures to both lexical and phrasal rules.

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“…This result is comparable to another algorithm proposed by Gilroy et al (2017). However, the strong restrictions of RGG make it too weak to model linguistic structures.…”

Section: Weakly Regular Graph Grammarsupporting

confidence: 67%

Exact yet Efficient Graph Parsing, Bi-directional Locality and the Constructivist Hypothesis

Ye¹,

Sun

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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“…In particular, it is exponential in the maximum degree of nodes in the input graph. The same holds for the parsing algorithm for regular graph grammars presented by Gilroy et al (2017). We also mention that another technique for efficient HRG parsing was resently developed by Drewes et al (2015Drewes et al ( , 2017.…”

Section: Introductionmentioning

confidence: 76%

Parsing Weighted Order-Preserving Hyperedge Replacement Grammars

Björklund

Drewes

Ericson

2019

Proceedings of the 16th Meeting on the Mathematics of Language

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“…Instead, as our search objective, we will use a simple asymptotic upper bound on the program's runtime, based on a folk theorem from the Datalog community that has a long history of use. Many NLP papers have analyzed the runtime of their algorithms using either this folk theorem or a more refined version given by McAllester (2002): Gildea (2011); Nederhof and Satta (2011); Gilroy et al (2017); Melamed (2003); Kuhlmann (2013); Nederhof and Sánchez-Sáez (2011); Büchse et al (2011); Lopez (2009); Eisner and Blatz (2007).…”

Section: Program Analysismentioning

confidence: 99%

Searching for More Efficient Dynamic Programs

Vieira¹,

Cotterell²,

Eisner³

2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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Computational models of human language often involve combinatorial problems. For instance, a probabilistic parser may marginalize over exponentially many trees to make predictions. Algorithms for such problems often employ dynamic programming and are not always unique. Finding one with optimal asymptotic runtime can be unintuitive, timeconsuming, and error-prone. Our work aims to automate this laborious process. Given an initial correct declarative program, we search for a sequence of semantics-preserving transformations to improve its running time as much as possible. To this end, we describe a set of program transformations, a simple metric for assessing the efficiency of a transformed program, and a heuristic search procedure to improve this metric. We show that in practice, automated search-like the mental search performed by human programmers-can find substantial improvements to the initial program. Empirically, we show that many speed-ups described in the NLP literature could have been discovered automatically by our system.* word(Y,I,K). z += β(root,0,N) * len(N). start β(X,I,K) += tmp(I,J,X,Z) * β(Z,J,K). β(X,I,K) += γ(X,Y) * word(Y,I,K). z += β(root,0,N) * len(N). tmp(I,J,X,Z) += β(Y,I,J) * γ(X,Y,Z).fold(1, [1, 2]) elim( 4)* word(Y,I,K). z += β(root,0,N) * len(N). tmp(I,J,X,Z) += β(Y,I,J) * γ(X,Y,Z). 21α("a") max= 1.22 α(J) max= α(I) * w(I,J). 23z += α("z").The second rule is recursive. For each possible value of J on the left-hand side, it defines α(J) by maximizing over assignments to the other variables on the right-hand side (namely I). Maximization is specified by max=, whereas summation in Example 1 was specified by +=.

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Parsing Graphs with Regular Graph Grammars

Cited by 11 publications

References 13 publications

Exact yet Efficient Graph Parsing, Bi-directional Locality and the Constructivist Hypothesis

Exact yet Efficient Graph Parsing, Bi-directional Locality and the Constructivist Hypothesis

Parsing Weighted Order-Preserving Hyperedge Replacement Grammars

Searching for More Efficient Dynamic Programs

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