Semi-supervised Gender Classification with Joint Textual and Social Modeling

@inproceedings{Li2016SemisupervisedGC,
  title={Semi-supervised Gender Classification with Joint Textual and Social Modeling},
  author={Shoushan Li and Bin Dai and Zhengxian Gong and Guodong Zhou},
  booktitle={COLING},
  year={2016}
}
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… CONTINUE READING

References

Publications referenced by this paper.

Similar Papers

Loading similar papers…