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

  • Liang Wang
  • Qi Li
  • Xuan Chen
  • Sujian LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10102)

Abstract

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.

Keywords

Multi-task learning Social media Neural network 

Notes

Acknowledgements

We thank all the anonymous reviewers for their insightful comments on this paper. This work was partially supported by National Natural Science Foundation of China (61273278 and 61572049).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Key Laboratory of Computational LinguisticsPeking University, MOEBeijingChina
  2. 2.School of InformationShandong University of Political Science and LawJinanChina
  3. 3.Collaborative Innovation Center for Language AbilityXuzhouChina

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