Inferring Public and Private Topics for Similar Events
Event detection, extraction, and tracking can help people to better understand the event that happened in the world. Previous research focuses on mining single event. In this paper, we propose a topic model to infer the public and private topic from a group of similar events. Aiming at the consistency and mapping of topics, this model discriminates public and private topics by using Bernoulli distribution to determine the source of words. Experiment on earthquake dataset shows that our proposed algorithm can induce the public and private topics acceptable by users.
The work is supported by the Natural Science Foundation of China (No. 61035004, No. 60973102), 863 High Technology Program (2011AA01A207), European Union 7th framework project FP7-288342, and THU-NUS NExT Co-Lab and the project cooperated with Chongqing research institute of science and technology.
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