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We present SAVITR, a system that leverages the information posted on theTwitter microblogging site to monitor and analyse emergency situations. Giventhat only a very small percentage of microblogs are geo-tagged, it is essentialfor such a system to extract locations from the text of the microblogs. Weemploy natural language processing techniques to infer the locations mentionedin the microblog text, in an unsupervised fashion and display it on a map-basedinterface. The system is designed for efficient performance, achieving anF-score of 0.79, and is approximately two orders of magnitude faster than otheravailable tools for location extraction.

SAVITR: A System for Real-time Location Extraction from Microblogs during Emergencies