@InProceedings{miura-EtAl:2016:WNUT,
  author    = {Miura, Yasuhide  and  Taniguchi, Motoki  and  Taniguchi, Tomoki  and  Ohkuma, Tomoko},
  title     = {A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {235--239},
  abstract  = {This paper describes a model that we submitted to W-NUT 2016 Shared task \#1:
	Geolocation Prediction in Twitter. Our model classifies a tweet or a user to a
	city using a simple neural networks structure with fully-connected layers and
	average pooling processes. From the findings of previous geolocation prediction
	approaches, we integrated various user metadata along with message texts and
	trained the model with them. In the test run of the task, the model achieved 
	the accuracy of 40.91% and the median distance error of 69.50 km in
	message-level prediction and the accuracy of 47.55% and the median distance
	error of 16.13 km in user-level prediction. These results are moderate
	performances in terms of accuracy and best performances in terms of distance.
	The results show a promising extension of neural networks based models for
	geolocation prediction where recent advances in neural networks can be added to
	enhance our current simple model.},
  url       = {http://aclweb.org/anthology/W16-3931}
}