@InProceedings{lau-EtAl:2017:I17-1,
  author    = {Lau, Jey Han  and  Chi, Lianhua  and  Tran, Khoi-Nguyen  and  Cohn, Trevor},
  title     = {End-to-end Network for Twitter Geolocation Prediction and Hashing},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {744--753},
  abstract  = {We propose an end-to-end neural network to predict the geolocation of a tweet.
	The network takes as input a number of raw Twitter metadata such as the tweet
	message and associated user account information. Our model is language
	independent, and despite minimal feature engineering, it is interpretable and
	capable of learning location indicative words and timing patterns. Compared to
	state-of-the-art systems, our model outperforms them by 2%-6%. Additionally, we
	propose extensions to the model to compress representation learnt by the
	network into binary codes. Experiments show that it produces compact codes
	compared to benchmark hashing algorithms. An implementation of the model is
	released publicly.},
  url       = {http://www.aclweb.org/anthology/I17-1075}
}