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

共 50 条

[31] Development of Multi-task Models for Emotion-Aware Gender Prediction
Suman, Chanchal
Singh, Abhishek
Saha, Sriparna
Bhattacharyya, Pushpak
2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022,
[32] Bayesian Multi-task Learning for Dynamic Time Series Prediction
Chandra, Rohitash
Cripps, Sally
2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018, : 390 - 397
[33] A MULTI-TASK LEARNING METHOD COMBINED WITH GAN FOR AERODYNAMIC PREDICTION
Zhang Guangbo
Hu Liwei
Zhang Jun
Xiang Yu
2022 19TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP), 2022,
[34] Conversion Prediction with Delayed Feedback: A Multi-task Learning Approach
Hou, Yilin
Zhao, Guangming
Liu, Chuanren
Zu, Zhonglin
Zhu, Xiaoqiang
2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021), 2021, : 191 - 199
[35] Spatiotemporal Multi-task Learning for Citywide Passenger Flow Prediction
Zhong, Runxing
Lv, Weifeng
Du, Bowen
Lei, Shuo
Huang, Runhe
2017 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTED, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI), 2017,
[36] A MIMO Channel Prediction Scheme Based on Multi-Task Learning
Li, Jing
Sun, DeChun
Liu, ZuJun
WIRELESS PERSONAL COMMUNICATIONS, 2020, 115 (03) : 1869 - 1880
[37] Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction
Fei, Hongliang
Tan, Shulong
Li, Ping
KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2019, : 834 - 842
[38] Disease outbreak prediction by data integration and multi-task learning
Bardak, Batuhan
Tan, Mehmet
2017 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (CIBCB), 2017, : 204 - 210
[39] Traffic Prediction With Missing Data: A Multi-Task Learning Approach
Wang, Ao
Ye, Yongchao
Song, Xiaozhuang
Zhang, Shiyao
Yu, James J. Q.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 24 (04) : 4189 - 4202
[40] Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
Phillips, John
Martinez, Julieta
Barsan, Ioan Andrei
Casas, Sergio
Sadat, Abbas
Urtasun, Raquel
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021, 2021, : 4677 - 4687

← 1 2 3 4 5 →