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        <title>Two-View Label Propagation to Semi-supervised Reader Emotion Classification</title>
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        <namePart type="given">Shoushan</namePart>
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    <name type="personal">
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            <namePart type="given">Yuji</namePart>
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    <abstract>In the literature, various supervised learning approaches have been adopted to address the task of reader emotion classification. However, the classification performance greatly suffers when the size of the labeled data is limited. In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm. Specifically, our approach depends on two word-document bipartite graphs to model the relationship among the samples in the two views respectively. Besides, the two bipartite graphs are integrated by linking each source text sample with its corresponding response text sample via a length-sensitive transition probability. In this way, our two-view label propagation approach to semi-supervised reader emotion classification largely alleviates the reliance on the strong sufficiency and independence assumptions of the two views, as required in co-training. Empirical evaluation demonstrates the effectiveness of our two-view label propagation approach to semi-supervised reader emotion classification.</abstract>
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%0 Conference Proceedings
%T Two-View Label Propagation to Semi-supervised Reader Emotion Classification
%A Li, Shoushan
%A Xu, Jian
%A Zhang, Dong
%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-two
%X In the literature, various supervised learning approaches have been adopted to address the task of reader emotion classification. However, the classification performance greatly suffers when the size of the labeled data is limited. In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm. Specifically, our approach depends on two word-document bipartite graphs to model the relationship among the samples in the two views respectively. Besides, the two bipartite graphs are integrated by linking each source text sample with its corresponding response text sample via a length-sensitive transition probability. In this way, our two-view label propagation approach to semi-supervised reader emotion classification largely alleviates the reliance on the strong sufficiency and independence assumptions of the two views, as required in co-training. Empirical evaluation demonstrates the effectiveness of our two-view label propagation approach to semi-supervised reader emotion classification.
%U https://aclanthology.org/C16-1249
%P 2647-2655

Markdown (Informal)

[Two-View Label Propagation to Semi-supervised Reader Emotion Classification](https://aclanthology.org/C16-1249) (Li et al., COLING 2016)

• Two-View Label Propagation to Semi-supervised Reader Emotion Classification (Li et al., COLING 2016)

ACL

• Shoushan Li, Jian Xu, Dong Zhang, and Guodong Zhou. 2016. Two-View Label Propagation to Semi-supervised Reader Emotion Classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2647–2655, Osaka, Japan. The COLING 2016 Organizing Committee.