Recognition and parsing of context-free languages in time n3

Younger, D. H.

doi:10.1016/s0019-9958(67)80007-x

Cited by 674 publications

(270 citation statements)

References 3 publications

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“…Reject filters give SGLR the ability to parse non-context-free languages such as a n b n c n [29]. The preference filter is intended to select one derivation from several alternative overlapping (ambiguous) derivations.…”

Section: Sglr and Rnglrmentioning

confidence: 99%

“…Parser run-time. Note that we have changed the original algorithm [29] for reject filtering at parser run-time for both SGLR and SRNGLR. The completeness and predictability of the filter have been improved.…”

Section: Rejectsmentioning

confidence: 99%

“…For this to work, the ambiguity must be detected before further reductions on A are done. Such ordering of the scheduling of reductions was proposed by Visser [29]. However, the proposed ordering is not complete.…”

Section: Rejectsmentioning

confidence: 99%

“…Scanners are often too simplistic to be able to deal with the actual syntax of a language and they prohibit modular implementation of parsers. Scannerless parsing [20,21,29] is a technique that avoids such issues that would be introduced by having a separate scanner [7]. Intuitively, a scannerless parser uses the power of context-free grammars instead of regular expressions to tokenize an input string.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we investigate the implementation of the Scannerless GLR (SGLR) parser provided with SDF [29,7]. It makes scannerless parsing feasible by rigorously limiting the non-determinism that is introduced by scannerless parsing using disambiguation filters.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Faster Scannerless GLR Parsing

Economopoulos

Klint

Vinju

2009

Lecture Notes in Computer Science

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Abstract. Analysis and renovation of large software portfolios requires syntax analysis of multiple, usually embedded, languages and this is beyond the capabilities of many standard parsing techniques. The traditional separation between lexer and parser falls short due to the limitations of tokenization based on regular expressions when handling multiple lexical grammars. In such cases scannerless parsing provides a viable solution. It uses the power of context-free grammars to be able to deal with a wide variety of issues in parsing lexical syntax. However, it comes at the price of less efficiency. The structure of tokens is obtained using a more powerful but more time and memory intensive parsing algorithm. Scannerless grammars are also more non-deterministic than their tokenized counterparts, increasing the burden on the parsing algorithm even further. In this paper we investigate the application of the Right-Nulled Generalized LR parsing algorithm (RNGLR) to scannerless parsing. We adapt the Scannerless Generalized LR parsing and filtering algorithm (SGLR) to implement the optimizations of RNGLR. We present an updated parsing and filtering algorithm, called SRNGLR, and analyze its performance in comparison to SGLR on ambiguous grammars for the programming languages C, Java, Python, SASL, and C++. Measurements show that SRNGLR is on average 33% faster than SGLR, but is 95% faster on the highly ambiguous SASL grammar. For the mainstream languages C, C++, Java and Python the average speedup is 16%.

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Section: Sglr and Rnglrmentioning

confidence: 99%

Section: Rejectsmentioning

confidence: 99%

Section: Rejectsmentioning

confidence: 99%