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Multi-task Learning for Gender and Age Prediction on Chinese Microblog

机译:中文微博上的性别和年龄预测的多任务学习

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摘要

The demographic attributes gender and age play an important role for social media applications. Previous studies on gender and age prediction mostly explore efficient features which are labor intensive. In this paper, we propose to use the multi-task convolutional neural network (MTCNN) model for predicting gender and age simultaneously on Chinese microblog. With MTCNN, we can effectively reduce the burden of feature engineering and explore common and unique representations for both tasks. Experimental results show that our method can significantly outperform the state-of-the-art baselines.
机译:性别和年龄的人口属性在社交媒体应用中起着重要作用。先前有关性别和年龄预测的研究大多探索劳动密集型的有效特征。在本文中,我们建议使用多任务卷积神经网络(MTCNN)模型在中国微博上同时预测性别和年龄。借助MTCNN,我们可以有效减轻特征工程的负担,并探索这两项任务的通用和唯一表示形式。实验结果表明,我们的方法可以大大优于最新的基准。

著录项

  • 来源
  • 会议地点 Kunming(CN)
  • 作者单位

    Key Laboratory of Computational Linguistics, Peking University, MOE, Beijing, China;

    Key Laboratory of Computational Linguistics, Peking University, MOE, Beijing, China;

    School of Information, Shandong University of Political Science and Law, Jinan, China;

    Key Laboratory of Computational Linguistics, Peking University, MOE, Beijing, China,Collaborative Innovation Center for Language Ability, Xuzhou, Jiangsu, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Multi-task learning; Social media; Neural network;

    机译:多任务学习;社交媒体;神经网络;

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