Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models
Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models
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DOI：
10.7551/mitpress/7503.003.0085
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发表时间：
2006-12
影响因子：
2.6
通讯作者：
Mark Johnson;T. Griffiths;S. Goldwater
中科院分区：
文献类型：
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作者：
Mark Johnson;T. Griffiths;S. Goldwater
本文介绍了适配文法，一类概率语言模型，推广了概率上下文无关文法（PCFG）。适配器语法增加了PCFG的概率规则与“适配器”，可以诱导连续使用之间的依赖关系。通过特定的适配器选择，基于Pitman-Yor过程，使用Dirichlet过程和分层Dirichlet过程的语言的非参数贝叶斯模型可以被写为简单的语法。我们提出了一个通用的推理算法适配器语法，使其易于定义和使用这样的模型，并说明如何现有的非参数贝叶斯模型可以在这个框架内表示。
This paper introduces adaptor grammars, a class of probabilistic models of language that generalize probabilistic context-free grammars (PCFGs). Adaptor grammars augment the probabilistic rules of PCFGs with "adaptors" that can induce dependencies among successive uses. With a particular choice of adaptor, based on the Pitman-Yor process, nonparametric Bayesian models of language using Dirichlet processes and hierarchical Dirichlet processes can be written as simple grammars. We present a general-purpose inference algorithm for adaptor grammars, making it easy to define and use such models, and illustrate how several existing nonparametric Bayesian models can be expressed within this framework.