Speeding up Generalized PSR Parsers by Memoization Techniques
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...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Open Publishing Association
2019-12-01
|
| Series: | Electronic Proceedings in Theoretical Computer Science |
| Online Access: | http://arxiv.org/pdf/1912.09609v1 |
| Summary: | 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. |
|---|---|
| ISSN: | 2075-2180 |