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Báo cáo khoa học: "PCFGs, Topic Models, Adaptor Grammars and Learning Topical Collocations and the Structure of Proper Names"

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Báo cáo khoa học: "PCFGs, Topic Models, Adaptor Grammars and Learning Topical Collocations and the Structure of Proper Names"

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This paper establishes a connection between two apparently very different kinds of probabilistic models. Latent Dirichlet Allocation (LDA) models are used as “topic models” to produce a lowdimensional representation of documents, while Probabilistic Context-Free Grammars (PCFGs) define distributions over trees. The paper begins by showing that LDA topic models can be viewed as a special kind of PCFG, so Bayesian inference for PCFGs can be used to infer Topic Models as well. Adaptor Grammars (AGs) are a hierarchical, non-parameteric Bayesian extension of PCFGs. ...

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Nội dung Text: Báo cáo khoa học: "PCFGs, Topic Models, Adaptor Grammars and Learning Topical Collocations and the Structure of Proper Names"

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