Semi-supervised Gender Classification with Joint Textual and Social Modeling

Shoushan Li, Bin Dai, Zhengxian Gong, Guodong Zhou


Abstract
In gender classification, labeled data is often limited while unlabeled data is ample. This motivates semi-supervised learning for gender classification to improve the performance by exploring the knowledge in both labeled and unlabeled data. In this paper, we propose a semi-supervised approach to gender classification by leveraging textual features and a specific kind of indirect links among the users which we call “same-interest” links. Specifically, we propose a factor graph, namely Textual and Social Factor Graph (TSFG), to model both the textual and the “same-interest” link information. Empirical studies demonstrate the effectiveness of the proposed approach to semi-supervised gender classification.
Anthology ID:
C16-1197
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2092–2100
Language:
URL:
https://aclanthology.org/C16-1197
DOI:
Bibkey:
Cite (ACL):
Shoushan Li, Bin Dai, Zhengxian Gong, and Guodong Zhou. 2016. Semi-supervised Gender Classification with Joint Textual and Social Modeling. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2092–2100, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Semi-supervised Gender Classification with Joint Textual and Social Modeling (Li et al., COLING 2016)
Copy Citation:
PDF:
https://aclanthology.org/C16-1197.pdf