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.
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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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Wang, L., Li, Q., Chen, X., Li, S. (2016). Multi-task Learning for Gender and Age Prediction on Chinese Microblog. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_16
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DOI: https://doi.org/10.1007/978-3-319-50496-4_16
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