@InProceedings{salehi-EtAl:2017:WNUT,
  author    = {Salehi, Bahar  and  Hovy, Dirk  and  Hovy, Eduard  and  S{\o}gaard, Anders},
  title     = {Huntsville, hospitals, and hockey teams: Names can reveal your location},
  booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {116--121},
  abstract  = {Geolocation is the task of identifying a social media user’s primary
	location, and in natural language processing, there is a growing literature on
	to what extent automated analysis of social media posts can help. However, not
	all content features are equally revealing of a user’s location.
	In this paper, we evaluate nine name entity (NE) types. Using various metrics,
	we find that GEO-LOC, FACILITY and SPORT-TEAM are more informative for
	geolocation than other NE types. Using these types, we improve geolocation
	accuracy and reduce distance error over various famous text-based methods.},
  url       = {http://www.aclweb.org/anthology/W17-4415}
}