A Particle Filter approach to dynamic Bayesian context estimation
نویسندگان
چکیده
This paper proposes a novel Bayesian long-distance language model that can capture subtopic shifts within a document. To model these subtopic flows, we introduce a latent mean shift model of natural language, and estimate its state space by a Particle Filter. Experiments on BNC corpus showed consistent improvements over the näıve context model that has been used so far.
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تاریخ انتشار 2005