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Topic Modeling: Beyond Bag-of-Words

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Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the ``bag-of-words’’ assumption, in which word order is ignored. Previously, these methods have not been combined. In this talk, I present a hierarchical generative probabilistic model that incorporates both $n$-gram statistics and latent topic variables, by extending a unigram topic model to include properties of a hierarchical Dirichlet bigram language model.

This talk is part of the Inference Group series.

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