  Social media plays a major role during and after major natural disasters
(e.g., hurricanes, large-scale fires, etc.), as people ``on the ground'' post
useful information on what is actually happening. Given the large amounts of
posts, a major challenge is identifying the information that is useful and
actionable. Emergency responders are largely interested in finding out what
events are taking place so they can properly plan and deploy resources. In this
paper we address the problem of automatically identifying important sub-events
(within a large-scale emergency ``event'', such as a hurricane). In particular,
we present a novel, unsupervised learning framework to detect sub-events in
Tweets for retrospective crisis analysis. We first extract noun-verb pairs and
phrases from raw tweets as sub-event candidates. Then, we learn a semantic
embedding of extracted noun-verb pairs and phrases, and rank them against a
crisis-specific ontology. We filter out noisy and irrelevant information then
cluster the noun-verb pairs and phrases so that the top-ranked ones describe
the most important sub-events. Through quantitative experiments on two large
crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we
demonstrate the effectiveness of our approach over the state-of-the-art. Our
qualitative evaluation shows better performance compared to our baseline.
