Abstract Meaning Representation for Sembanking

By L. Banarescu, Claire Bonial, Shu Cai, M. Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, Nathan Schneider. 2013

1,587 citations recorded by the source corpus.

Subfields: Natural Language Processing

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Citations in the corpus

Break It Down: A Question Understanding Benchmark (2020, background): “Last, AMR (Banarescu et al., 2013) is a meaning representation for sentences.”
Tailor: Generating and Perturbing Text with Semantic Controls (2021, background): “Ab-stract Ab-stract Meaning Representation (Banarescu et al., 2013; Mager et al., 2020) is an alternative semantic representation worth exploring for data perturbation, as it may further enable controls on entity recursions (Damonte and Cohen, 2019), though expressing such relationships is…”
Probing for semantic evidence of composition by means of simple classification tasks (2016, background): “The importance of semantic role information is well-recognized both in linguistics and in NLP— for the latter in the form of tasks such as abstract meaning representation (AMR) (Banarescu et al., 2013).”
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (2018, neutral): “They have been studied extensively (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Wong and Mooney, 2007; Das et al., 2010; Liang et al., 2011; Banarescu et al., 2013; Artzi and Zettlemoyer, 2013; Reddy et al., 2014; Berant and Liang, 2014; Dong and Lapata, 2016).”
Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers (2021, methodology): “, 2014) to map an utterance into a structured meaning representations, such as LFs, SQL, and abstract meaning representation (Banarescu et al., 2013).”
Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language (2015, background): “Abstract Meaning Representation (Banarescu et al., 2013) can be viewed as an extension of PropBank with additional semantic information.”
Automatically Solving Number Word Problems by Semantic Parsing and Reasoning (2015, background): “Among all meaning representation languages, AMR (Banarescu et al., 2013) is most similar to DOL.”
Generating Semantically Precise Scene Graphs from Textual Descriptions for Improved Image Retrieval (2015, methodology): “Parsing to graph-based representations Representing semantic information with graphs has recently experienced a resurgence caused by the development of the Abstract Meaning Representation (AMR) (Banarescu et al., 2013) which was followed by several works on parsing natural language sentences to AMR ”
NaturalLI: Natural Logic Inference for Common Sense Reasoning (2014, background): “Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 534–545, October 25-29, 2014, Doha, Qatar. c©2014 Association for Computational Linguistics”
Learning executable semantic parsers for natural language understanding (2016, background): “Abstract meaning representation (AMR) [3] is one popular representation backed by an extension linguistic annotation eﬀort.”
Evaluation of Text Generation: A Survey (2020, background): “For instance, Task 9 of the SemEval 2017 challenge was (sem, 2017) on text generation from AMR (Abstract Meaning Representation), which has focused on generating valid English sentences given AMR (Banarescu et al., 2013) annotation structure.”
Question Answering as Global Reasoning Over Semantic Abstractions (2018, methodology): “…constructed using semi-automatical methods, Khot, Sabhar-wal, and Clark (2017) use Open IE subject-verb-object relations (Etzioni et al. 2008), Banarescu et al. (2013) use AMR annotators (Wang et al. 2015), and Krishnamurthy, Tafjord, and Kembhavi (2016) use a semantic parser (Zettlemoyer and…”
DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations (2022, positive): “In AMR graphs, entities and concepts are shown as nodes and their relations are depicted with various relation edges (Banarescu et al., 2013).”
Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems (2017, positive): “In this work, we use AMR 19 to carefully select the meaningful context words.”
GPT-too: A Language-Model-First Approach for AMR-to-Text Generation (2020, background): “Abstract Meaning Representation (AMR) (Banarescu et al., 2013) is a rooted, directed, acyclic graph with labeled edges (relations) and nodes (concepts) expressing “who is doing what to whom”.”
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering (2018, background): “Among the second category, there are many parsers which proposes to convert English sentences into the AMR representation (Banarescu et al. 2013).”
Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework (2017, neutral): “…Recursion Semantics (Copestake et al., 2005), Episodic Logic (Schubert and Hwang, 2000), Combinatory Categorical Grammar (Steedman, 2000), Semantic Role Labeling (Gildea and Jurafsky, 2002), Framenet Parsing (Fillmore et al., 2003) and Abstract Meaning Representation (Banarescu et al., 2013).”
Cross-lingual Name Tagging and Linking for 282 Languages (2017, methodology): “We exploit the English Abstract Meaning Representation (AMR) corpus (Banarescu et al., 2013) which includes both name tagging and linking annotations for ﬁne-grained entity types to train an automatic classiﬁer.”