Natural Language Processing for Analyzing Disaster Recovery Trends Expressed in Large Text Corpora

We are developing a new natural language processing (NLP) method to facilitate analysis of text corpora that describe long-term recovery. The aim of the method is to allow users to measure the degree that user-specified propositions about potential issues are embodied within the corpora, serving as a proxy for the disaster recovery process. The presented method employs a statistical syntax-based semantic matching model and was trained on a standard, publicly available training dataset. We applied the NLP method to a news story corpus that describes the recovery of Christchurch, New Zealand after the 2010-2011 Canterbury earthquake sequence. We used the model to compute semantic measurements of multiple potential recovery issues as expressed in the Christchurch news corpus that span 2011 to 2016. We evaluated method outputs through a user study involving twenty professional emergency managers. User study results show that the model can be effective when applied to a disaster-related news corpus. 85% of study participants were interested in a way to measure recovery issue propositions in news or other corpora. We are encouraged by the potential for future applications of our NLP method for after-action learning, recovery decision making, and disaster research.