X-AMR Annotation Tool
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917620765163520 |
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| author | Ahmed, Shafiuddin Rehan Cai, Jon Z. Palmer, Martha Martin, James H. |
| author_facet | Ahmed, Shafiuddin Rehan Cai, Jon Z. Palmer, Martha Martin, James H. |
| contents | This paper presents a novel Cross-document Abstract Meaning Representation (X-AMR) annotation tool designed for annotating key corpus-level event semantics. Leveraging machine assistance through the Prodigy Annotation Tool, we enhance the user experience, ensuring ease and efficiency in the annotation process. Through empirical analyses, we demonstrate the effectiveness of our tool in augmenting an existing event corpus, highlighting its advantages when integrated with GPT-4. Code and annotations: https://github.com/ahmeshaf/gpt_coref |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15407 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | X-AMR Annotation Tool Ahmed, Shafiuddin Rehan Cai, Jon Z. Palmer, Martha Martin, James H. Computation and Language Artificial Intelligence This paper presents a novel Cross-document Abstract Meaning Representation (X-AMR) annotation tool designed for annotating key corpus-level event semantics. Leveraging machine assistance through the Prodigy Annotation Tool, we enhance the user experience, ensuring ease and efficiency in the annotation process. Through empirical analyses, we demonstrate the effectiveness of our tool in augmenting an existing event corpus, highlighting its advantages when integrated with GPT-4. Code and annotations: https://github.com/ahmeshaf/gpt_coref |
| title | X-AMR Annotation Tool |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2403.15407 |