用于上下文无关语言解析的Pr/T-Net模型

Erqing Xu
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引用次数: 2

摘要

本文提出了一个上下文无关语法谓词转换网络(CFG-Pr/T-Net)模型,用于分析上下文无关语言。这些位置被定义为表示解析过程的状态。定义了转换所携带的条件和动作,以便可以重写正在处理的非终结符号,并且可以进行推导。数据结构(作为令牌的特征)的定义使得不断增长的派生可以存储在令牌中。应用实例表明,Pr/T-Net模型能够成功地进行句法分析。CFG-Pr/T-Net克服了下推自动机的局限性,即只能判断字符串是否属于给定的上下文无关语言,而不能回答有关语法结构的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Pr/T-Net model for context-free language parsing
This paper presents a Context-Free Grammar-Predicate/Transition-Net (CFG-Pr/T-Net) model for the parsing of context-free language. The places are defined to represent the states of the parsing process. The conditions and actions carried by the transitions are defined such that non-terminal symbols being processed can be rewritten and the derivation can be made to grow. Data structures, as the personalities of the token, are defined such that the growing derivation can be stored in the token. An application example was examined and the result shows that the Pr/T-Net model can do syntactic parsing successfully. The CFG-Pr/T-Net overcomes the limitation of pushdown automata, which only judged whether a string belonged to a given context free language, but were not able to answer the question concerning syntactic structure.
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