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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/C16-1197.pdf