Thread Labeling for News Event

doi:10.1007/s12204-013-1416-z

Abstract

Abstract: 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.

Key words: news event| topic labeling| latent Dirichlet allocation (LDA)

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.

关键词: news event| topic labeling| latent Dirichlet allocation (LDA)

CLC Number:

TP 391

YAN Ze-hua (闫泽华), LI Fang* (李芳). Thread Labeling for News Event[J]. Journal of shanghai Jiaotong University (Science), 2013, 18(4): 418-424.

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