Machine learning and natural language processing for humanitarian response: a view from the field
This event took place on Wednesday 17 October 2018 at 11:30
Drawing on real-life case studies from Nepal, Indonesia, and Kenya, I will provide an overview of how crowdsourced and social media data are used or ignored in humanitarian response and the challenges they pose for practitioners. Designed in order to respond to these challenges, I will present early stage software prototypes using the Crisis Event Extraction Service, an open-source web API that automatically classifies crowdsourced and social media data during crisis situations. The API provides annotations for crisis-related documents, event types and information categories to help speed and prioritize the delivery of humanitarian aid. Speaking as a practitioner, I will also propose avenues for impactful research and design to help increase the adoption of new tools and methods.
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