@InProceedings{li-EtAl:2016:COLING7,
  author    = {Li, Shoushan  and  Dai, Bin  and  Gong, Zhengxian  and  Zhou, Guodong},
  title     = {Semi-supervised Gender Classification with Joint Textual and Social Modeling},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2092--2100},
  abstract  = {In gender classification, labeled data is often limited while unlabeled data is
	ample. This motivates semi-supervised learning for gender classification to
	improve the performance by exploring the knowledge in both labeled and
	unlabeled data. In this paper, we propose a semi-supervised approach to gender
	classification by leveraging textual features and a specific kind of indirect
	links among the users which we call “same-interest” links. Specifically, we
	propose a factor graph, namely Textual and Social Factor Graph (TSFG), to model
	both the textual and the “same-interest” link information.                   
	Empirical
	studies demonstrate the effectiveness of the proposed approach to
	semi-supervised gender classification.},
  url       = {http://aclweb.org/anthology/C16-1197}
}