@InProceedings{miura-EtAl:2017:I17-2,
  author    = {Miura, Yasuhide  and  Taniguchi, Tomoki  and  Taniguchi, Motoki  and  Misawa, Shotaro  and  Ohkuma, Tomoko},
  title     = {Using Social Networks to Improve Language Variety Identification with Neural Networks},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {263--270},
  abstract  = {We propose a hierarchical neural network model for language variety
	identification that integrates information from a social network. Recently,
	language variety identification has enjoyed heightened popularity as an
	advanced task of language identification. The proposed model uses additional
	texts from a social network to improve language variety identification from two
	perspectives. First, they are used to introduce the effects of homophily.
	Secondly, they are used as expanded training data for shared layers of the
	proposed model. By introducing information from social networks, the model
	improved its accuracy by 1.67-5.56. Compared to state-of-the-art baselines,
	these improved performances are better in English and comparable in Spanish.
	Furthermore, we analyzed the cases of Portuguese and Arabic when the model
	showed weak performances, and found that the effect of homophily is likely to
	be weak due to sparsity and noises compared to languages with the strong
	performances.},
  url       = {http://www.aclweb.org/anthology/I17-2045}
}