关于使用框架学习上下文无关语法

G. L. Prajapati, N. Chaudhari, M. Chandwani
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引用次数: 0

摘要

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。
On Learning Context-Free Grammars Using Skeletons
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.
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