Local Likelihood Modeling of Temporal Text Streams

author: Guy Lebanon, Purdue University
published: Aug. 7, 2008,   recorded: July 2008,   views: 3241


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Temporal text data is often generated by a time-changing process or distribution. Such a drift in the underlying distribution cannot be captured by stationary likelihood techniques. We consider the application of local likelihood methods to generative and conditional modeling of temporal document sequences. We examine the asymptotic bias and variance and present an experimental study using the RCV1 dataset containing a temporal sequence of Reuters news stories.

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