Distributional Learning of Context-Free and Multiple Context-Free Grammars
Distributional Learning of Context-Free and Multiple Context-Free Grammars
复制标题
上下文无关和多重上下文无关语法的分布式学习
DOI：
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发表时间：
2016
期刊：
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通讯作者：
Ryo Yoshinaka
中科院分区：
文献类型：
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作者：
A. Clark;Ryo Yoshinaka
本章回顾了语法推理中的分布式学习在上下文无关和多上下文无关语法学习中的最新进展。我们讨论了分布式学习的基本原理，并提出了两类表示，原始和对偶，原始方法使用基于字符串或字符串集的非终端和对偶方法使用基于上下文或上下文集的非终端。然后，我们提出了学习算法的基础上，这两个模型使用各种学习范式，然后讨论自然扩展到轻度上下文敏感的形式主义，使用多个上下文无关的语法作为代表的形式主义。
This chapter reviews recent progress in distributional learning in grammatical inference as applied to learning context-free and multiple context-free grammars. We discuss the basic principles of distributional learning, and present two classes of representations, primal and dual, where primal approaches use nonterminals based on strings or sets of strings and dual approaches use nonterminals based on contexts or sets of contexts. We then present learning algorithms based on these two models using a variety of learning paradigms, and then discuss the natural extension to mildly context-sensitive formalisms, using multiple context-free grammars as a representative formalism.