  Social media platforms provide continuous access to user generated content
that enables real-time monitoring of user behavior and of events. The
geographical dimension of such user behavior and events has recently caught a
lot of attention in several domains: mobility, humanitarian, or
infrastructural. While resolving the location of a user can be straightforward,
depending on the affordances of their device and/or of the application they are
using, in most cases, locating a user demands a larger effort, such as
exploiting textual features. On Twitter for instance, only 2% of all tweets are
geo-referenced. In this paper, we present a system for zoomed-in grounding
(below city level) for short messages (e.g., tweets). The system combines
different natural language processing and machine learning techniques to
increase the number of geo-grounded tweets, which is essential to many
applications such as disaster response and real-time traffic monitoring.
