@inproceedings{li-etal-2016-semi,
    title = "Semi-supervised Gender Classification with Joint Textual and Social Modeling",
    author = "Li, Shoushan  and
      Dai, Bin  and
      Gong, Zhengxian  and
      Zhou, Guodong",
    editor = "Matsumoto, Yuji  and
      Prasad, Rashmi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/C16-1197/",
    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 {\textquotedblleft}same-interest{\textquotedblright} links. Specifically, we propose a factor graph, namely Textual and Social Factor Graph (TSFG), to model both the textual and the {\textquotedblleft}same-interest{\textquotedblright} link information. Empirical studies demonstrate the effectiveness of the proposed approach to semi-supervised gender classification."
}