Chapter? of The Handbook of Applied Bayesian Analysis

Chapter? of The Handbook of Applied Bayesian Analysis (2009)

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by Percy Liang , Michael I. Jordan , Dan Klein , Tony O’hagan , Mike West

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@MISC{Liang09chapter?of,
    author = {Percy Liang and Michael I. Jordan and Dan Klein and Tony O’hagan and Mike West},
    title = {Chapter? of The Handbook of Applied Bayesian Analysis},
    year = {2009}
}

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Abstract

Probabilistic context-free grammars (PCFGs) have played an important role in the modeling of syntax in natural language processing and other applications, but choosing the proper model complexity is often difficult. We present a nonparametric Bayesian generalization of the PCFG based on the hierarchical Dirichlet process (HDP). In our HDP-PCFG model, the effective complexity of the grammar can grow with increasing data. We describe an efficient variational inference algorithm for our model and present experiments on both a synthetic grammar induction task and a large-scale natural language parsing task.

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