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    <abstract>In this paper, we introduce CAMRA (Copilot for AMR Annotatations), a cutting-edge web-based tool designed for constructing Abstract Meaning Representation (AMR) from natural language text. CAMRA offers a novel approach to deep lexical semantics annotation such as AMR, treating AMR annotation akin to coding in programming languages. Leveraging the familiarity of programming paradigms, CAMRA encompasses all essential features of existing AMR editors, including example lookup, while going a step further by integrating Propbank roleset lookup as an autocomplete feature within the tool. Notably, CAMRA incorporates AMR parser models as coding co-pilots, greatly enhancing the efficiency and accuracy of AMR annotators.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-demo.35</identifier>
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%0 Conference Proceedings
%T CAMRA: Copilot for AMR Annotation
%A Cai, Jon
%A Ahmed, Shafiuddin Rehan
%A Bonn, Julia
%A Wright-Bettner, Kristin
%A Palmer, Martha
%A Martin, James H.
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F cai-etal-2023-camra
%X In this paper, we introduce CAMRA (Copilot for AMR Annotatations), a cutting-edge web-based tool designed for constructing Abstract Meaning Representation (AMR) from natural language text. CAMRA offers a novel approach to deep lexical semantics annotation such as AMR, treating AMR annotation akin to coding in programming languages. Leveraging the familiarity of programming paradigms, CAMRA encompasses all essential features of existing AMR editors, including example lookup, while going a step further by integrating Propbank roleset lookup as an autocomplete feature within the tool. Notably, CAMRA incorporates AMR parser models as coding co-pilots, greatly enhancing the efficiency and accuracy of AMR annotators.
%R 10.18653/v1/2023.emnlp-demo.35
%U https://aclanthology.org/2023.emnlp-demo.35
%U https://doi.org/10.18653/v1/2023.emnlp-demo.35
%P 381-388

Markdown (Informal)

[CAMRA: Copilot for AMR Annotation](https://aclanthology.org/2023.emnlp-demo.35) (Cai et al., EMNLP 2023)

• CAMRA: Copilot for AMR Annotation (Cai et al., EMNLP 2023)

ACL

• Jon Cai, Shafiuddin Rehan Ahmed, Julia Bonn, Kristin Wright-Bettner, Martha Palmer, and James H. Martin. 2023. CAMRA: Copilot for AMR Annotation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 381–388, Singapore. Association for Computational Linguistics.