AMR Parsing
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Benchmarks
LDC2017T10
LDC2020T02
LDC2014T12
LDC2015E86
Bio
LDC2014T12:
New3
The Little Prince
Most implemented
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
An Incremental Parser for Abstract Meaning Representation
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing
AMR Parsing via Graph-Sequence Iterative Inference
Inducing and Using Alignments for Transition-based AMR Parsing
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
Papers
Bridging Natural Language and ASP: A Hybrid Approach Using LLMs and AMR Parsing
Answer Set Programming (ASP) is a declarative programming paradigm based on logic programming and non-monotonic reasoning. It is a tremendously powerful tool for describing and solving combinatorial problems. Like any ot…
AMR ParsingHSGM: Hierarchical Segment-Graph Memory for Scalable Long-Text Semantics
Semantic parsing of long documents remains challenging due to quadratic growth in pairwise composition and memory requirements. We introduce \textbf{Hierarchical Segment-Graph Memory (HSGM)}, a novel framework that decom…
Semantic Role LabelingEvent ExtractionSemantic ParsingAMR ParsingEvaluation of Finetuned LLMs in AMR Parsing
AMR (Abstract Meaning Representation) is a semantic formalism that encodes sentence meaning as rooted, directed, acyclic graphs, where nodes represent concepts and edges denote semantic relations. Finetuning decoder only…
AMR ParsingReassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based Encoding
Sequence-to-sequence models are widely used to train Abstract Meaning Representation (Banarescu et al., 2013, AMR) parsers. To train such models, AMR graphs have to be linearized into a one-line text format. While Penman…
Abstract Meaning RepresentationAMR ParsingShould Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing
Cross-lingual AMR parsing is the task of predicting AMR graphs in a target language when training data is available only in a source language. Due to the small size of AMR training data and evaluation data, cross-lingual…
AMR ParsingMeta-LearningAdapting Abstract Meaning Representation Parsing to the Clinical Narrative -- the SPRING THYME parser
This paper is dedicated to the design and evaluation of the first AMR parser tailored for clinical notes. Our objective was to facilitate the precise transformation of the clinical notes into structured AMR expressions, …
Abstract Meaning RepresentationAMR ParsingData AugmentationDomain Adaptation