Location extraction from disaster-related microblogs

Location information is critical to understanding the impact of a disaster, including where the damage is, where people need assistance and where help is available. We investigate the feasibility of applying Named Entity Recognizers to extract locations from microblogs, at the level of both geo-location and point-of-interest. Our experimental results show that such tools once retrained on microblog data have great potential to detect the where information, even at the granularity of point-of-interest.