Discovering event evolution graphs from newswires

In this paper, we propose an approach to automatically mine event evolution graphs from newswires on the Web. Event evolution graph is a directed graph in which the vertices and edges denote news events and the evolutions between events respectively, in a news affair. Our model utilizes the content similarity between events and incorporates temporal proximity and document distributional proximity as decaying functions. Our approach is effective in presenting the inside developments of news affairs along the timeline, which can facilitate users' information browsing tasks. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: information filtering, retrieval models, clustering General Terms Algorithms, Experimentation, Measurement Keywords Web content mining, event evolution, event evolution graph, knowledge management

• Type: Conference
• Year: 2006
• Where: WWW
• Authors: Christopher C. Yang, Xiaodong Shi
• Keywords: Document Distributional Proximity, Event Evolution Graph, Event Evolution Graphs, Internet Technology, WWW 2006