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A new dynamic topic tracking model was proposed based on Bayesian belief network,which is used as the representation model in this paper.We used time distance to quantify temporal information which is then used to dy— namically adjust feature weight.A weight decay function was given to deal with the long-time disappearing features.If the weight of a feature is lower than the given threshold after decaying,the feature will be viewed as redundant informa— tion.TDT4 corpora and DET curves were used to run experiments.W e firstly obtained the optimal time distance thresh— old 0/and the threshold 8 to determ ine whether a feature iS redundant information~Experimental results show that the tracking performance of dynamic topic models can be effectively improved by using tempora1 weight.

动态话题追踪中的时序权重 (Temporal Weight in Dynamic Topic Tracking).