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.
CITATION STYLE
Wang, L., Li, Q., Chen, X., & Li, S. (2016). Multi-task learning for gender and age prediction on Chinese microblog. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10102, pp. 189–200). Springer Verlag. https://doi.org/10.1007/978-3-319-50496-4_16
Mendeley helps you to discover research relevant for your work.