关键词: 事件聚合, 动态发展规律, 动态时序模型, 静态建模

Abstract: Internet is an important source of information and a lot of news websites have become the main places for generating and propagating information. Most information on Internet is irregular, noisy and dynamic. To organize and aggregate the information effectively, this paper proposes a method combining a static model and a dynamic model for network event aggregation. The static model clusters textual documents based on the similarity of content, and the dynamic one is to describe the dynamic process of network events based on Hidden Markov Models（HMMs） and determines whether the current news follow the law of event development. The proposed method describes network events based on both static content and dynamics instead of only the former used in a lot of previous studies, which benefits the recognition of network events. The experiment is conducted on a real-world dataset, and the experimental results demonstrate the effectiveness of the proposed method in network event aggregation.

Key words: event aggregation, dynamic development law, dynamic time series model, static modeling