Semi-supervised Gender Classification with Joint Textual and Social Modeling

COLING 2016 Shoushan LiBin DaiZhengxian GongGuodong Zhou

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... (read more)

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