  We present SAVITR, a system that leverages the information posted on the
Twitter microblogging site to monitor and analyse emergency situations. Given
that only a very small percentage of microblogs are geo-tagged, it is essential
for such a system to extract locations from the text of the microblogs. We
employ natural language processing techniques to infer the locations mentioned
in the microblog text, in an unsupervised fashion and display it on a map-based
interface. The system is designed for efficient performance, achieving an
F-score of 0.79, and is approximately two orders of magnitude faster than other
available tools for location extraction.
