Stochastic And-Or Grammars: A Unified Framework and Logic Perspective
Abstract
Stochastic And-Or grammars (AOG) extend traditional stochastic grammars of language to model other types of data such as images and events. In this paper we propose a representation framework of stochastic AOGs that is agnostic to the type of the data being modeled and thus unifies various domain-specific AOGs. Many existing grammar formalisms and probabilistic models in natural language processing, computer vision, and machine learning can be seen as special cases of this framework. We also propose a domain-independent inference algorithm of stochastic context-free AOGs and show its tractability under a reasonable assumption. Furthermore, we provide two interpretations of stochastic context-free AOGs as a subset of probabilistic logic, which connects stochastic AOGs to the field of statistical relational learning and clarifies their relation with a few existing statistical relational models.
Cite
@article{arxiv.1506.00858,
title = {Stochastic And-Or Grammars: A Unified Framework and Logic Perspective},
author = {Kewei Tu},
journal= {arXiv preprint arXiv:1506.00858},
year = {2016}
}