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.
|Title of host publication||Advances in Neural Information Processing Systems 19|
|Editors||B. Schölkopf, J. Platt, T. Hoffman|
|Place of Publication||Cambridge, MA|
|Number of pages||8|
|Publication status||Published - 2007|