| Challenge: | Abstract Meaning Representation (AMR) is a formalism for deep lexical semantic representation. |
| Approach: | They introduce a web-based tool for constructing AMR from natural language text . CAMRA incorporates AMR parser models as coding co-pilots . |
| Outcome: | The proposed tool is based on the prototyping of existing AMR editors and integrates Propbank roleset lookup as an autocomplete feature. |
Similar Papers
A Survey of AMR Applications (2024.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation that takes the form of a rooted, directed graph. |
| Approach: | They analyze more than 100 papers which use Abstract Meaning Representation (AMR) they highlight the range of applications for which AMR has been harnessed and techniques for incorporating it . they also highlight broader AMR engineering patterns and outline areas of future work that seem ripe for AMR incorporation. |
| Outcome: | The results highlight the range of applications for which AMR has been harnessed and the techniques for incorporating it into those applications. |
UMR-Writer: A Web Application for Annotating Uniform Meaning Representations (2021.emnlp-demo)
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| Challenge: | Uniform Meaning Representations (UMRs) are graph-based semantic representations that can be used to annotate text. |
| Approach: | They present a web-based application for annotating Uniform Meaning Representations (UMR) they propose to use a graph-based cross-linguistically applicable semantic representation to annotate sentences and documents. |
| Outcome: | The proposed tool is based on a graph-based, cross-linguistically applicable semantic representation that can be used to annotate text. |
A Structured Syntax-Semantics Interface for English-AMR Alignment (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) annotations do not require explicit mapping between elements of an AMR and the corresponding elements of the sentence that evoke them. |
| Approach: | They devised an expressive framework to align AMR graphs to dependency graphs . their framework explains how 97% of AMR edges are evoked by words or syntax . |
| Outcome: | The proposed framework explains how 97% of AMR edges are evoked by words or syntax. |
LiDARR: Linking Document AMRs with Referents Resolvers (2025.acl-demo)
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Jon Cai, Kristin Wright-Bettner, Zekun Zhao, Shafiuddin Rehan Ahmed, Abijith Trichur Ramachandran, Jeffrey Flanigan, Martha Palmer, James Martin
| Challenge: | Abstract Meaning Representation (AMR) is a formalism for semantic representation of natural language text. |
| Approach: | They propose a web tool for semantic annotation at the document level using Abstract Meaning Representation (AMR) it integrates an AMR-to-surface alignment model and a coreference resolution model into the tool . |
| Outcome: | The proposed tool simplifies the creation of knowledge graphs from natural language documents . it integrates an AMR-to-surface alignment model and coreference resolution model . |
Dialogue-AMR: Abstract Meaning Representation for Dialogue (2020.lrec-1)
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Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron Artstein, David Traum, Clare Voss
| Challenge: | Abstract Meaning Representation (AMR) does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context. |
| Approach: | They propose a schema that enriches Abstract Meaning Representation (AMR) it provides a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. |
| Outcome: | The proposed schema provides a semantic representation for facilitating Natural Language Understanding (NLU) in human-robot dialogue systems. |
Abstract Meaning Representation of Constructions: The More We Include, the Better the Representation (L18-1)
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Claire Bonial, Bianca Badarau, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Tim O’Gorman, Martha Palmer, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) uses a flexible pattern or template of multiple lexical items to provide semantic representation of certain constructions. |
| Approach: | They propose to expand the AMR project's lexicon of predicate senses to include entries for a growing set of constructions. |
| Outcome: | The proposed approach provides coverage for the annotation of certain types of constructions. |
Annotating Abstract Meaning Representations for Spanish (L18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation language for natural language processing. |
| Approach: | They propose a method that would lay the groundwork for building a large semantic bank for Spanish . they propose to use a database to annotate AMRs for other languages . |
| Outcome: | The proposed method would lay the groundwork for building a large semantic bank for Spanish and guide those who would like to implement it for other languages. |
Factorising AMR generation through syntax (N19-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic annotation framework which abstracts away from the surface form of text to capture the core 'who did what to whom' structure. |
| Approach: | They propose to decompose the generation process into two steps: first generate a syntactic structure, and then generate the surface form. |
| Outcome: | The proposed approach generates meaning-preserving syntactic paraphrases of the same graph, as judged by humans. |
Cross-Lingual Abstract Meaning Representation Parsing (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) research has focused on English . Qualitative analysis shows that the new parsers overcome structural differences between the languages. |
| Approach: | They propose to use an AMR parser for English and parallel corpora to learn AMR for Italian, Spanish, German and Chinese. |
| Outcome: | The proposed method overcomes structural differences between the target languages and requires no gold standard data. |
“You Are An Expert Linguistic Annotator”: Limits of LLMs as Analyzers of Abstract Meaning Representation (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) demonstrate proficiency and fluency in the use of language, but do they have the linguistic knowledge to serve as an expert linguistic annotator? |
| Approach: | They examine the successes and limitations of large language models using the Abstract Meaning Representation (AMR) parsing formalism. |
| Outcome: | The proposed models can reproduce the basic format of AMR, as well as some core event, argument, and modifier structure, but they have virtually no fully accurate parses. |