用记忆技术加速广义PSR语法分析器
Speeding up Generalized PSR Parsers by Memoization Techniques
摘要：
预测移位归约（PSR）分析的超边替换（HR）语法是非常有效的，但限于一个子类的明确的HR语法。为了克服这一限制，我们最近扩展PSR解析广义PSR（GPSR）解析富田风格的广义LR解析沿线。不幸的是，GPSR解析器在没有手动调优的情况下效率太低。本文提出了使用记忆化技术来加速GPSR解析器，而不需要任何手动调整，并已实现在图解析器蒸馏器Grappa。我们提出了一些示例语言的运行时间测量，它们在解析有效图形时显示出一些数量级的显着速度。但是，记忆化技术在解析无效图时或者在应该确定对模糊输入图的所有解析时没有帮助。
英文摘要：
Predictive shift-reduce (PSR) parsing for hyperedge replacement (HR) grammars is very efficient, but restricted to a subclass of unambiguous HR grammars. To overcome this restriction, we have recently extended PSR parsing to generalized PSR (GPSR) parsing along the lines of Tomita-style generalized LR parsing. Unfortunately, GPSR parsers turned out to be too inefficient without manual tuning. This paper proposes to use memoization techniques to speed up GPSR parsers without any need of manual tuning, and which has been realized within the graph parser distiller Grappa. We present running time measurements for some example languages; they show a significant speed up by some orders of magnitude when parsing valid graphs. But memoization techniques do not help when parsing invalid graphs or if all parses of an ambiguous input graph shall be determined.
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