Between a Rock and a Hard Place – Uniform Parsing for Hyperedge Replacement DAG Grammars

Björklund, Henrik; Drewes, Frank; Ericson, Petter

doi:10.1007/978-3-319-30000-9_40

Cited by 23 publications

(40 citation statements)

References 4 publications

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“…Perhaps we need less expressivity than HRG but more than RGG. A possible alternative would be to consider Restricted DAG Grammars (RDG; Björklund et al 2016). Parsing for a fixed such grammar can be achieved in quadratic time with respect to the input graph.…”

Section: Discussionmentioning

confidence: 99%

Parsing Graphs with Regular Graph Grammars

Gilroy

Lopez²,

Maneth³

2017

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

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Recently, several datasets have become available which represent natural language phenomena as graphs. Hyperedge Replacement Languages (HRL) have been the focus of much attention as a formalism to represent the graphs in these datasets. Chiang et al. (2013) prove that HRL graphs can be parsed in polynomial time with respect to the size of the input graph. We believe that HRL are more expressive than is necessary to represent semantic graphs and we propose the use of Regular Graph Languages (RGL; Courcelle 1991), which is a subfamily of HRL, as a possible alternative. We provide a topdown parsing algorithm for RGL that runs in time linear in the size of the input graph.

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Section: Discussionmentioning

confidence: 99%

Parsing Graphs with Regular Graph Grammars

Gilroy

Lopez²,

Maneth³

2017

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

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“…This property implies connectedness of the terminal subgraph, a property that both Tree-like Grammars (Matheja et al, 2015) and Restricted DAG Grammars (Björklund et al, 2016) share, although both of these formalisms allow nodes that are connected only to nonterminals, which is forbidden in RGG. We suspect that all three families of languages are incomparable.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Recently, two new graph grammar formalisms have been defined which are also restricted forms of HRL: Tree-like Grammars (TLG; Matheja et al 2015) and Restricted DAG Grammars (RDG; Björklund et al 2016). TLGs are claimed to be in SCFL, but the relationship of RDG to SCFL is unknown.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 15th Meeting on the Mathematics of Language

Gierasimczuk

Szymanik

2017

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The volume contains eleven regular papers and two invited papers. It also includes an abstract of a third invited talk. The regular papers were selected from a total of 23 submissions, using the EasyChair conference management system. The conference benefited from the financial support of the British Logic Colloquium (http://www. blc-logic.org) and of AYLIEN (http://aylien.com), which we gratefully acknowledge.Last but not least, we would like to express our sincere gratitude to all the reviewers for MOL 2017 and to all the people who helped with the local organization. Partee (1986) claimed without proof that the function BE is the only homomorphism that makes the Partee triangle commute. This paper shows that this claim is incorrect unless "homomorphism" is understood as "complete homomorphism." It also shows that BE and A are the inverses of each other on certain natural assumptions. IntroductionIn a famous and influential paper, Partee (1986) discussed type-shifting operators for NP interpretations, including lift, ident and BE: lift = λxλP. P (x), ident = λxλy. [y = x], BE = λPλx. P(λy. [y = x]).She pointed out that these operators satisfy the equality BE • lift = ident, so the following diagram, now often referred to as the Partee triangle, commutes. Partee declared that BE is "natural" because of the following two "facts." Fact 1. BE is a homomorphism from e, t , t to e, t viewed as Boolean structures, i.e., BE(P 1 P 2 ) = BE(P 1 ) BE(P 2 ), BE(P 1 P 2 ) = BE(P 1 ) BE(P 2 ), BE(¬P 1 ) = ¬BE(P 1 ).Fact 2. BE is the unique homomorphism that makes the diagram commute.While Fact 1 is immediate, Fact 2 is not obvious. Partee (1986) nevertheless did not give a proof of Fact 2, but only a note saying, "Thanks to Johan van Benthem for the fact, which he knows how to prove but I don't." Meanwhile, van Benthem (1986) referred to Partee's work and stated Fact 2 on p. 68, but gave no proof either. Despite this quite obscure exposition, because of the classic status of Partee's and van Benthem's work, I suspect that many linguists take Fact 2 for granted while unable to explain it. Not only is this unfortunate, but it is actually as expected, because Fact 2 turns out to be not quite correct unless "homomorphism" is read as "complete homomorphism." The main purpose of this paper is to rectify this detrimental situation.Van Benthem (1986) took the domain of entities to be finite, writing, "Our general feeling is that natural language requires the use of finite models only" (p. 7). Fact 2 is indeed correct on this assumption. However, natural language has predicates like natural number whose extensions are obviously infinite. Also, if we take the domain of portions of matter in the sense of Link (1983) to be a nonatomic join-semilattice, then the domain of entities will surely be infinite, whether countable or uncountable. It is a fact that a single sentence of natural language, albeit only finitely long, can talk about an infinite number of entities, as exemplified in (1).(1) a. Every natural number is odd or even...

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“…AMR parsing systems that focus on modeling the graph aspect of the AMR includes JAMR (Flanigan et al, 2014(Flanigan et al, , 2016aZhou et al, 2016), which treats AMR parsing as a procedure for searching for the Maximum Spanning Connected Subgraphs (MSCGs) from an edge-labeled, directed graph of all possible relations. Parsers based on Hyperedge Replacement Grammars (HRG) (Chiang et al, 2013;Björklund et al, 2016;Groschwitz et al, 2015) put more emphasis on modeling the formal properties of the AMR graph. One practical implementation of HRG-based parsing is that of (Peng et al, 2015;Peng and Gildea, 2016).…”

Section: Related Workmentioning

confidence: 99%

Getting the Most out of AMR Parsing

Wang

Xue

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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This paper proposes to tackle the AMR parsing bottleneck by improving two components of an AMR parser: concept identification and alignment. We first build a Bidirectional LSTM based concept identifier that is able to incorporate richer contextual information to learn sparse AMR concept labels. We then extend an HMM-based word-to-concept alignment model with graph distance distortion and a rescoring method during decoding to incorporate the structural information in the AMR graph. We show integrating the two components into an existing AMR parser results in consistently better performance over the state of the art on various datasets.

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Between a Rock and a Hard Place – Uniform Parsing for Hyperedge Replacement DAG Grammars

Cited by 23 publications

References 4 publications

Parsing Graphs with Regular Graph Grammars

Parsing Graphs with Regular Graph Grammars

Proceedings of the 15th Meeting on the Mathematics of Language

Getting the Most out of AMR Parsing

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