Empowering crisis information extraction through actionability event schemata and domain-adaptive pre-training
One of the persistent challenges in crisis detection is inferring actionable information to support emergency response. Existing methods focus on situational awareness but often lack actionable insights. This study proposes a holistic approach to implementing an actionability extraction system on so...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2024
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/9721 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | One of the persistent challenges in crisis detection is inferring actionable information to support emergency response. Existing methods focus on situational awareness but often lack actionable insights. This study proposes a holistic approach to implementing an actionability extraction system on social media, including requirement gathering, selection of machine learning tasks, data preparation, and integration with existing resources, providing guidance for governments, civil services, emergency workers, and researchers on supplementing existing channels with actionable information from social media. Our solution leverages an actionability schema and domain-adaptive pre-training, improving upon the state-of-the-art model by 5.5% and 10.1% in micro and macro F1 scores. |
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