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        <title>Semi-supervised Gender Classification with Joint Textual and Social Modeling</title>
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        <namePart type="given">Shoushan</namePart>
        <namePart type="family">Li</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Zhengxian</namePart>
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    <name type="personal">
        <namePart type="given">Guodong</namePart>
        <namePart type="family">Zhou</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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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.</abstract>
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        <date>2016-12</date>
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            <start>2092</start>
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%0 Conference Proceedings
%T Semi-supervised Gender Classification with Joint Textual and Social Modeling
%A Li, Shoushan
%A Dai, Bin
%A Gong, Zhengxian
%A Zhou, Guodong
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F li-etal-2016-semi
%X 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.
%U https://aclanthology.org/C16-1197
%P 2092-2100

Markdown (Informal)

[Semi-supervised Gender Classification with Joint Textual and Social Modeling](https://aclanthology.org/C16-1197) (Li et al., COLING 2016)

• Semi-supervised Gender Classification with Joint Textual and Social Modeling (Li et al., COLING 2016)

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.