Multilingual Viewpoint Detection from news comments

Bei Shi, Wai Lam, Lidong Bing, Yinqing Xu
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Abstract

In this paper, we investigate the task of Multilingual Viewpoint Detection (MVD) on multilingual news reader comments. To tackle the MVD task, we propose a new probabilistic graphical model called VDMC to discover latent common viewpoints from multilingual news reader comments. Our VDMC model can cope with the language gap and detect common multilingual viewpoints. To learn the model parameters, we incorporate bilingual constraints into the variational Expectation-Maximization (EM) method. Experimental results show that our VDMC model can resolve the MVD task effectively and outperform the state-of-the-art method.
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基于新闻评论的多语言视点检测
本文研究了多语种新闻读者评论的多语种视点检测问题。为了解决MVD任务,我们提出了一个新的概率图形模型,称为VDMC,从多语言新闻读者评论中发现潜在的共同观点。我们的VDMC模型可以处理语言差异并检测常见的多语言视点。为了学习模型参数,我们将双语约束纳入变分期望最大化(EM)方法中。实验结果表明,我们的VDMC模型可以有效地解决MVD任务,并且优于现有的方法。
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