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

被引：5

作者：

Wang, Liang ^{[1
]}

Li, Qi ^{[1
]}

Chen, Xuan ^{[2
]}

Li, Sujian ^{[1
,3
]}

机构：

[1] Peking Univ, Key Lab Computat Linguist, MOE, Beijing, Peoples R China

[2] Shandong Univ Polit Sci & Law, Sch Informat, Jinan, Peoples R China

[3] Collaborat Innovat Ctr Language Abil, Xuzhou, Jiangsu, Peoples R China

来源：

NATURAL LANGUAGE UNDERSTANDING AND INTELLIGENT APPLICATIONS (NLPCC 2016) | 2016年 / 10102卷

基金：

中国国家自然科学基金;

关键词：

Multi-task learning; Social media; Neural network;

D O I：

10.1007/978-3-319-50496-4_16

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页码：189 / 200

页数：12

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