mlsanityData to 7 Oct 2026

AMR Dependency Parsing with a Typed Semantic Algebra

arXiv 1805.11465 ↗First posted 29 May 2018cs.CLcited by 17

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Abstract (arXiv)

We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 May 2018.

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Citing papers per year: 2021: 6; 2022: 2; 2023: 4; 2024: 2; 2025: 0; 2026: 3.

17 papers.

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