Topic tracking with Bayesian belief network |
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| Authors: | Jian-min Xu Shu-fang Wu Yu Hong |
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| Affiliation: | 1. Hebei University College of Management, Hebei Baoding 071002, China;2. Hebei Software Institute Department of Information Engineering, Hebei Baoding 071000, China;3. Soochow University School of Computer and Technology, Suzhou 215006, China |
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| Abstract: | The task of topic tracking is to monitor a stream of stories and find all subsequent stories that discuss the same topic. Using Bayesian belief network we give three topic tracking models: a static topic model BSTM and two dynamic topic models BDTM-I, BDTM-II. BDTM-II merges the advantages of BSTM and BDTM-I, has better tracking performance than the former two, and effectively alleviates topic drift phenomenon. Applying unrelated coming stories to update BDTM-I and BDTM-II can filter noises existed in topics. Experiments on TDT corpora show that BSTM decreases (Cdet)norm by 5.5% comparing to VSM, BDTM-II decreases (Cdet)norm by 6.3% and 6.0% comparing to BSTM and BDTM-I respectively, using unrelated stories can improve the tracking performance. |
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| Keywords: | Bayesian belief network Topic tracking Static topic model Dynamic topic model |
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