CAMRA: Copilot for AMR Annotation
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916132772904960 |
|---|---|
| author | Cai, Jon Z. Ahmed, Shafiuddin Rehan Bonn, Julia Wright-Bettner, Kristin Palmer, Martha Martin, James H. |
| author_facet | Cai, Jon Z. Ahmed, Shafiuddin Rehan Bonn, Julia Wright-Bettner, Kristin Palmer, Martha Martin, James H. |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_10928 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CAMRA: Copilot for AMR Annotation Cai, Jon Z. Ahmed, Shafiuddin Rehan Bonn, Julia Wright-Bettner, Kristin Palmer, Martha Martin, James H. Computation and Language Artificial Intelligence 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 |
| title | CAMRA: Copilot for AMR Annotation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2311.10928 |