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Thread Labeling for News Event

Thread Labeling for News Event
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摘要 Automatic thread labeling for news events can help people know different aspects of a news event. In this paper, we present a method to label threads of a news event. We use latent Dirichlet allocation (LDA) topic model to extract news threads from news corpus. Our method first selects the thread words subset then extracts phrases based on co-occurrence calculation. The extracted phrase is then used as a label of a news thread. Experimental results show that about 60% of generated labels visualize the meaningful aspects of a news event. These labels can help people fast to capture many different aspects of a news event. Automatic thread labeling for news events can help people know different aspects of a news event. In this paper, we present a method to label threads of a news event. We use latent Dirichlet allocation （LDA） topic model to extract news threads from news corpus. Our method first selects the thread words subset then extracts phrases based on co-occurrence calculation. The extracted phrase is then used as a label of a news thread. Experimental results show that about 60% of generated labels visualize the meaningful aspects of a news event. These labels can help people fast to capture many different aspects of a news event.
作者 闫泽华 李芳
机构地区 Department of Computer Science and Engineering
出处 《Journal of Shanghai Jiaotong university(Science)》 EI 2013年第4期418-424,共7页 上海交通大学学报（英文版）
基金 the National Natural Science Foundation of China(No.60873134)
关键词 计算机 信息处理 应用程序 CNNIC news event, topic labeling, latent Dirichlet allocation （LDA）
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献28

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Journal of Shanghai Jiaotong university(Science)
2013年 第4期

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