A probabilistic model for retrospective news event detection

Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles...

• Type: Conference
• Year: 2005
• Where: SIGIR
• Authors: Zhiwei Li, Bin Wang, Mingjing Li, Wei-Ying Ma
• Keywords: Data Enriched Environments, Few Research Works, SIGIR 2005, Time Information