CAMRA: Copilot for AMR Annotation
This tool addresses the problem of time-consuming and error-prone AMR annotation for researchers and linguists, representing an incremental improvement over existing editors.
The paper tackles the challenge of constructing Abstract Meaning Representation (AMR) from text by introducing CAMRA, a web-based tool that treats AMR annotation like coding and integrates parser models as co-pilots, resulting in enhanced efficiency and accuracy for annotators.
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. To demonstrate the tool's capabilities, we provide a live demo accessible at: https://camra.colorado.edu