@InProceedings{phillips-EtAl:2017:RepL4NLP,
  author    = {Phillips, Lawrence  and  Shaffer, Kyle  and  Arendt, Dustin  and  Hodas, Nathan  and  Volkova, Svitlana},
  title     = {Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {201--210},
  abstract  = {Language in social media is a dynamic system, constantly evolving and adapting,
	with words and concepts rapidly emerging, disappearing, and changing their
	meaning. These changes can be estimated using word representations in context,
	over time and across locations. A number of methods have been proposed to track
	these spatiotemporal changes but no general method exists to evaluate the
	quality of these representations. Previous work largely focused on qualitative
	evaluation, which we improve by proposing a set of visualizations that
	highlight changes in text representation over both space and time. We
	demonstrate usefulness of novel spatiotemporal representations to explore and
	characterize specific aspects of the corpus of tweets collected from European
	countries over a two-week period centered around the terrorist attacks in
	Brussels in March 2016. In addition, we quantitatively evaluate spatiotemporal
	representations by feeding them into a downstream classification task -- event
	type prediction. Thus, our work is the first to provide both intrinsic
	(qualitative) and extrinsic (quantitative) evaluation of text representations
	for spatiotemporal trends.},
  url       = {http://www.aclweb.org/anthology/W17-2624}
}