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- W2164328751 abstract "This paper presents a new Bayesian topical trend analysis. We regard the parameters of topic Dirichlet priors in latent Dirichlet allocation as a function of document timestamps and optimize the parameters by a gradient-based algorithm. Since our method gives similar hyperparameters to the documents having similar timestamps, topic assignment in collapsed Gibbs sampling is affected by timestamp similarities. We compute TFIDF-based document similarities by using a result of collapsed Gibbs sampling and evaluate our proposal by link detection task of Topic Detection and Tracking." @default.
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- W2164328751 date "2009-11-02" @default.
- W2164328751 modified "2023-09-26" @default.
- W2164328751 title "Dynamic hyperparameter optimization for bayesian topical trend analysis" @default.
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- W2164328751 doi "https://doi.org/10.1145/1645953.1646242" @default.
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