Robust Subgraph Generation Improves Abstract Meaning Representation Parsing

Werling, Keenon; Angeli, Gabor; Manning, Christopher D.

doi:10.48550/arxiv.1506.03139

Cited by 1 publication

(1 citation statement)

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“…They use a sequence labeling algorithm to identify concepts and frame the relation prediction task as a constrained combinatorial optimization problem. Werling et al (2015) notice that the difficult bit is the concept identification and propose a better way to handle that task: an action classifier to generate concepts by applying predetermined actions. Other proposals involve a synchronous hyperedge replacement grammar solution (Peng et al, 2015), a syntaxbased machine translation approach (Pust et al, 2015) where a grammar of string-to-tree rules is created after reducing AMR graphs to trees by removing all reentrancies, a CCG system that first parses sentences into lambda-calculus representations (Artzi et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

An Incremental Parser for Abstract Meaning Representation

Damonte¹,

Cohen²,

Satta³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

141

238

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Meaning Representation (AMR) is a semantic representation for natural language that embeds annotations related to traditional tasks such as named entity recognition, semantic role labeling, word sense disambiguation and co-reference resolution. We describe a transition-based parser for AMR that parses sentences leftto-right, in linear time. We further propose a test-suite that assesses specific subtasks that are helpful in comparing AMR parsers, and show that our parser is competitive with the state of the art on the LDC2015E86 dataset and that it outperforms state-of-the-art parsers for recovering named entities and handling polarity.

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