Semi-supervised Gender Classification with Joint Textual and Social Modeling

Shoushan Li, Bin Dai, Zhengxian Gong, Guodong Zhou

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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)
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https://preview.aclanthology.org/teach-a-man-to-fish/C16-1197.pdf
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