mlsanityData to 7 Oct 2026

An Incremental Parser for Abstract Meaning Representation

arXiv 1608.06111 ↗First posted 22 Aug 2016cs.CLcited by 34

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Abstract (arXiv)

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 left-to-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.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2016.

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Citing papers per year: 2021: 13; 2022: 5; 2023: 9; 2024: 3; 2025: 4.

34 papers.

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  1. 2022SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Featurescited by 33
  2. 2021AMR Parsing with Action-Pointer Transformercited by 25
  3. 2021Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsingcited by 25
  4. 2023SMATCH++: Standardized and Extended Evaluation of Semantic Graphscited by 19
  5. 2021Process-Level Representation of Scientific Protocols with Interactive Annotationcited by 16
  6. 2021Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsingcited by 15
  7. 2021Ensembling Graph Predictions for AMR Parsingcited by 15
  8. 2021DocAMR: Multi-Sentence AMR Representation and Evaluationcited by 12
  9. 2023ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translationcited by 11
  10. 2021Hierarchical Curriculum Learning for AMR Parsingcited by 10
  11. 2023Incorporating Graph Information in Transformer-based AMR Parsingcited by 7
  12. 2021Grounded Graph Decoding Improves Compositional Generalization in Question Answeringcited by 6
22 more
  1. 2023AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suitecited by 6
  2. 2022ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRscited by 5
  3. 2024Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representationscited by 5
  4. 2021One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasetscited by 3
  5. 2023Discourse Representation Structure Parsing for Chinesecited by 3
  6. 2023AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsingcited by 3
  7. 2025SR-LLM: Rethinking the Structured Representation in Large Language Modelcited by 3
  8. 2021Levi Graph AMR Parser using Heterogeneous Attentioncited by 2
  9. 2021StreamSide: A Fully-Customizable Open-Source Toolkit for Efficient Annotation of Meaning Representationscited by 2
  10. 2022Cross-domain Generalization for AMR Parsingcited by 2
  11. 2024Understanding Enthymemes in Argument Maps: Bridging Argument Mining and Logic-based Argumentationcited by 2
  12. 2025Prior Lessons of Incremental Dialogue and Robot Action Management for the Age of Language Modelscited by 2
  13. 2025Survey of Abstract Meaning Representation: Then, Now, Futurecited by 2
  14. 2021A Transition-based Parser for Unscoped Episodic Logical Formscited by 1
  15. 2021Making Better Use of Bilingual Information for Cross-Lingual AMR Parsingcited by 1
  16. 2023AMR Parsing with Causal Hierarchical Attention and Pointerscited by 1
  17. 2024Estimating Text Similarity based on Semantic Concept Embeddingscited by 1
  18. 2022Transition-based Abstract Meaning Representation Parsing with Contextual Embeddingscited by 0
  19. 2022Strictly Breadth-First AMR Parsingcited by 0
  20. 2023Widely Interpretable Semantic Representation: Frameless Meaning Representation for Broader Applicabilitycited by 0
  21. 2023Guiding AMR Parsing with Reverse Graph Linearizationcited by 0
  22. 2025Evaluation of Finetuned LLMs in AMR Parsingcited by 0