On Learning Context-Free Grammars Using Skeletons

G. L. Prajapati, N. Chaudhari, M. Chandwani
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Abstract

In 1992, Sakakibara introduced a well-known approach for learning context-free grammars from positive samples of structural descriptions (skeletons). In particular, Sakakibarapsilas approach uses reversible tree automata construction algorithm RT. Here, we introduce a modification of the learning algorithm RT for reversible tree automata. With respect to n, where n is the sum of the sizes of the input skeletons, our modification for RT, called e_RT, needs O(n3) operations and achieves the storage space saving by a factor of O(n) over RT. Using our e_RT, we give an algorithm e_RC to learn reversible context-free grammars from positive samples of their structural descriptions. Furthermore, we modify e_RC to learn extended reversible context-free grammars from positive-only examples. Finally, we present summary of our experiments carried out to see how our results compare with those of Sakakibara, which also confirms our approach as efficient and useful.
关于使用框架学习上下文无关语法
1992年,Sakakibara介绍了一种著名的方法,用于从结构描述的正面样本(骨架)中学习上下文无关语法。特别是,Sakakibarapsilas方法使用可逆树自动机构建算法RT。在这里,我们介绍了对可逆树自动机学习算法RT的修改。对于n,其中n是输入骨架大小的总和,我们对RT的修改称为e_RT,需要O(n3)次操作,并通过O(n) / RT节省存储空间。使用我们的e_RT,我们给出了一个算法e_RC从其结构描述的正样本中学习可逆的上下文无关语法。此外,我们修改e_RC以从仅限正的示例中学习扩展的可逆上下文无关语法。最后,我们总结了我们所进行的实验,看看我们的结果与Sakakibara的结果如何比较,这也证实了我们的方法是有效和有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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