以普遍H-批评为基础,为没有环境、没有概率、无环境的语法学习进行歧视性学习 (Discriminative Learning for Probabilistic Context-Free Grammars based on Generalized H-Criterion)

We present a formal framework for the development of a family of discriminative learning algorithms for Probabilistic Context-Free Grammars (PCFGs) based on a generalization of criterion-H. First of all, we propose the H-criterion as the objective function and the Growth Transformations as the optimization method, which allows us to develop the final expressions for the estimation of the parameters of the PCFGs. And second, we generalize the H-criterion to take into account the set of reference interpretations and the set of competing interpretations, and we propose a new family of objective functions that allow us to develop the expressions of the estimation transformations for PCFGs.

翻译：我们提出了一个正式框架,用于在标准-H的通用基础上,为无背景语法(PCFGs)制定一套有区别的学习算法。首先,我们建议将H标准作为客观功能,将增长转型作为优化方法,使我们能够为估计PCFG的参数制定最后表达方式。其次,我们对H标准作了概括,以考虑到一套参考解释和一套相互竞争的解释,我们提出了一套新的客观功能,使我们能够为PCFGs拟订估算变化的表述方式。