Multi-task Learning for Gender and Age Prediction on Chinese Microblog
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.
multi-task learning social media neural network
Liang Wang Qi Li Xuan Chen Sujian Li
Key Laboratory of Computational Linguistics,Peking University,MOE,China School of Information,Shandong University of Political Science and Law,Jinan,China Key Laboratory of Computational Linguistics,Peking University,MOE,China;Collaborative Innovation Cen
国际会议
第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)
昆明
英文
1-11
2016-12-02(万方平台首次上网日期,不代表论文的发表时间)