Online multiscale dynamic topic models

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Online multiscale dynamic topic models
We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, some words may be used consistently over one hundred years, while other words emerge and disappear over periods of a few days. Thus, in the proposed model, current topicspecific distributions over words are assumed to be generated based on the multiscale word distributions of the previous epoch. Considering both the long-timescale dependency as well as the short-timescale dependency yields a more robust model. We derive efficient online inference procedures based on a stochastic EM algorithm, in which the model is sequentially updated using newly obtained data; this means that past data are not required to make the inference. We demonstrate the effectiveness of the proposed method in terms of predictive performance and computational efficiency by examining collections of real documents with timestamps. Catego...
Tomoharu Iwata, Takeshi Yamada, Yasushi Sakurai, N
Added 15 Aug 2010
Updated 15 Aug 2010
Type Conference
Year 2010
Where KDD
Authors Tomoharu Iwata, Takeshi Yamada, Yasushi Sakurai, Naonori Ueda
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