@InProceedings{miura-EtAl:2017:Long,
  author    = {Miura, Yasuhide  and  Taniguchi, Motoki  and  Taniguchi, Tomoki  and  Ohkuma, Tomoko},
  title     = {Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1260--1272},
  abstract  = {We propose a novel geolocation prediction model using a complex neural network.
	Geolocation prediction in social media has attracted many researchers to use
	information of various types. Our model unifies text, metadata, and user
	network representations with an attention mechanism to overcome previous
	ensemble approaches. In an evaluation using two open datasets, the proposed
	model exhibited a maximum 3.8% increase in accuracy and a maximum of 6.6%
	increase in accuracy$@$161 against previous models. We further analyzed several
	intermediate layers of our model, which revealed that their states capture some
	statistical characteristics of the datasets.},
  url       = {http://aclweb.org/anthology/P17-1116}
}