  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.
