Towards Dual Approaches for Learning Context-Free Grammars Based on Syntactic Concept Lattices
Towards Dual Approaches for Learning Context-Free Grammars Based on Syntactic Concept Lattices
复制标题
DOI：
10.1007/978-3-642-22321-1_37
复制
发表时间：
2011-07
期刊：
影响因子：
--
通讯作者：
Ryo Yoshinaka
中科院分区：
文献类型：
--
作者：
Ryo Yoshinaka
最近关于语法推理的研究已经证明了“分布式学习”对于学习上下文无关和上下文敏感语言的好处。分布式学习建模并利用学习目标语言中的字符串和上下文之间的关系。主要有两种方法。一个，我们称之为primal，构造非终结符，其语言的特征是字符串。另一种，我们称之为dual，使用上下文来表征猜想语法的非终结符的语言。本文展示和讨论了这些方法的二重性，提出了一些强大的学习算法沿着。
Recent studies on grammatical inference have demonstrated the benefits of “distributional learning” for learning context-free and context-sensitive languages. Distributional learning models and exploits the relation between strings and contexts in the language of the learning target. There are two main approaches. One, which we callprimal, constructs nonterminals whose language is characterized by strings. The other, which we calldual, uses contexts to characterize the language of a nonterminal of the conjecture grammar. This paper demonstrates and discusses the duality of those approaches by presenting some powerful learning algorithms along the way.