When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916447337316352 |
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| author | Noriega-Atala, Enrique Vacareanu, Robert Ashton, Salena Torres Pyarelal, Adarsh Morrison, Clayton T. Surdeanu, Mihai |
| author_facet | Noriega-Atala, Enrique Vacareanu, Robert Ashton, Salena Torres Pyarelal, Adarsh Morrison, Clayton T. Surdeanu, Mihai |
| contents | We introduce a neural architecture finetuned for the task of scenario context generation: The relevant location and time of an event or entity mentioned in text. Contextualizing information extraction helps to scope the validity of automated finings when aggregating them as knowledge graphs. Our approach uses a high-quality curated dataset of time and location annotations in a corpus of epidemiology papers to train an encoder-decoder architecture. We also explored the use of data augmentation techniques during training. Our findings suggest that a relatively small fine-tuned encoder-decoder model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurate predict the relevant scenario information of a particular entity or event. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07567 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context Noriega-Atala, Enrique Vacareanu, Robert Ashton, Salena Torres Pyarelal, Adarsh Morrison, Clayton T. Surdeanu, Mihai Computation and Language Artificial Intelligence We introduce a neural architecture finetuned for the task of scenario context generation: The relevant location and time of an event or entity mentioned in text. Contextualizing information extraction helps to scope the validity of automated finings when aggregating them as knowledge graphs. Our approach uses a high-quality curated dataset of time and location annotations in a corpus of epidemiology papers to train an encoder-decoder architecture. We also explored the use of data augmentation techniques during training. Our findings suggest that a relatively small fine-tuned encoder-decoder model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurate predict the relevant scenario information of a particular entity or event. |
| title | When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.07567 |