@InProceedings{rahimi-baldwin-cohn:2017:EMNLP2017,
  author    = {Rahimi, Afshin  and  Baldwin, Timothy  and  Cohn, Trevor},
  title     = {Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {167--176},
  abstract  = {We propose a method for embedding two-dimensional locations in a continuous
	vector space using a neural network-based model incorporating mixtures of
	Gaussian distributions, presenting two model variants for text-based
	geolocation and lexical dialectology. Evaluated over Twitter data, the proposed
	model outperforms conventional regression-based geolocation and provides a
	better estimate of uncertainty. We also show the effectiveness of the
	representation for predicting words from location in lexical dialectology, and
	evaluate it using the DARE dataset.},
  url       = {https://www.aclweb.org/anthology/D17-1016}
}