Automated Generation of Interorganizational Disaster Response Networks through Information Extraction
This work addresses the challenge of efficient network generation for disaster response practitioners, though it appears incremental as it applies existing NLP techniques to a specific domain.
The paper tackles the problem of reducing the effort in generating Stakeholder Collaboration Networks (SCNs) for disaster response by proposing an automated approach using Named Entity Recognition (NER) to extract stakeholders and interactions from text, and it demonstrates feasibility in a Hurricane Harvey case study, significantly reducing interpretation and data collection workloads.
When a disaster occurs, maintaining and restoring community lifelines subsequently require collective efforts from various stakeholders. Aiming at reducing the efforts associated with generating Stakeholder Collaboration Networks (SCNs), this paper proposes a systematic approach to reliable information extraction for stakeholder collaboration and automated network generation. Specifically, stakeholders and their interactions are extracted from texts through Named Entity Recognition (NER), one of the techniques in natural language processing. Once extracted, the collaboration information is transformed into structured datasets to generate the SCNs automatically. A case study of stakeholder collaboration during Hurricane Harvey was investigated and it had demonstrated the feasibility and applicability of the proposed method. Hence, the proposed approach was proved to significantly reduce practitioners' interpretation and data collection workloads. In the end, discussions and future work are provided.