Generalised Parsing: Some Costs

Johnstone, Adrian; Scott, Elizabeth; Economopoulos, Giorgios

doi:10.1007/978-3-540-24723-4_7

Cited by 18 publications

(10 citation statements)

References 15 publications

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“…We have previously reported some experimental results comparing the recogniser versions of BRNGLR and Earley's algorithm [15]. Here we present results comparing the parser versions of these algorithms.…”

Section: Cubic Lookahead Earley Parsing and Brnglr Parsingmentioning

confidence: 93%

Recognition is not parsing — SPPF-style parsing from cubic recognisers

Scott

Johnstone

2010

Science of Computer Programming

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a b s t r a c tIn their recogniser forms, the Earley and RIGLR algorithms for testing whether a string can be derived from a grammar are worst-case cubic on general context free grammars (CFG). Earley gave an outline of a method for turning his recognisers into parsers, but it turns out that this method is incorrect. Tomita's GLR parser returns a shared packed parse forest (SPPF) representation of all derivations of a given string from a given CFG but is worstcase unbounded polynomial order. The parser version of the RIGLR algorithm constructs Tomita-style SPPFs and thus is also worst-case unbounded polynomial order. We have given a modified worst-case cubic GLR algorithm, that, for any string and any CFG, returns a binarised SPPF representation of all possible derivations of a given string. In this paper we apply similar techniques to develop worst-case cubic Earley and RIGLR parsing algorithms.Since Knuth's seminal 1960's work on LR parsing [16] was extended to LALR parsers by DeRemer [6,5], the Computer Science community has been able to automatically generate parsers for a very wide class of context free languages. However, many parsers are still written manually, either using tool support or even completely by hand. This is partly because in some application areas such as natural language processing and bioinformatics we do not have the luxury of designing the language so that it is amenable to known parsing techniques, but also it is clear that left to themselves computer language designers do not naturally write LR(1) grammars. Indeed, designers sometimes prefer to write ambiguous grammars because they feel that they are easier to read.A grammar not only defines the syntax of a language, it is also the starting point for the definition of the semantics, and the grammar which facilitates semantic definition is not usually the one which is LR(1). This is illustrated by the development of the Java Standard. The first edition of the Java Language Specification [8] contains a detailed discussion of the need to modify the grammar used to define the syntax and semantics in the main part of the standard to make it LALR(1) for compiler generation purposes. In the third version of the standard [9] the compiler version of the grammar is written in EBNF and is (unnecessarily) ambiguous, illustrating the difficulty of making correct transformations. Given the difficulty in constructing natural LR(1) grammars that support desired semantics, the general parsing techniques, such as the CYK [25], Earley [7] and GLR [24] algorithms, developed for natural language processing are also of interest to the wider computer science community.When using grammars as the starting point for semantics definition, we distinguish between recognisers which simply determine whether or not a given string is in the language defined by a given grammar, and parsers which also return some form of derivation of the string, if one exists. In their basic forms the CYK-, Earley-and GLR-inspired RIGLR [21] algorithms are recognisers, while standard G...

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Section: Cubic Lookahead Earley Parsing and Brnglr Parsingmentioning

confidence: 93%

Recognition is not parsing — SPPF-style parsing from cubic recognisers

Scott

Johnstone

2010

Science of Computer Programming

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“…In particular, we have compared Tomita's original algorithm, and Farshi's modification of it, with the RNGLR algorithm [24,7]; we have compared the Reduction Incorporated GLR algorithm with the other GLR algorithms [23,11]; we have developed resolved right nullable tables and considered their application to RNGLR and LR parsing [22]. We have also made comparative studies of these algorithms when running with a variety of types of LR tables and discussed some of the phenomena that underlie the space and time complexities of these parsers [14,15,13]. More recently we have reported on approaches to the removal of embedded recursion in grammars, a necessary precursor to automaton construction for RIGLR parsers [12,8].…”

Section: Grammars For Standard Programming Languagesmentioning

confidence: 97%

Proofs and pedagogy; science and systems: The grammar tool box

Johnstone

Scott

2007

Science of Computer Programming

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GTB (the Grammar Tool Box) is the tool that underpins our investigations into generalised parsing. Our goal is to produce a system that supports systematic investigation of various styles of generalised parsing in a way that allows meaningful comparisons between them in a repeatable and easily accessible fashion whilst also allowing: (i) new theoretical ideas to be generated and explored; (ii) production quality parsers to be generated and (iii) humane pedagogy. GTB comprises a language (LC) with various kinds of built-in grammar and automata related objects, and a set of black-box methods written in C++ that provide implementations of grammar transforms, automata construction algorithms, parsing and recognition algorithms, and a variety of visualisation aids. In this paper we focus on the overall rationale for the GTB framework; the GTB design goals; and some detailed operational flows that are supported by GTB.The late Roger Needham famously said that research should be done with a shovel, not tweezers [20], and the computing community both academic and industrial has certainly taken this maxim to heart. However, as archaeologists know, once past the surface layers, using a shovel is dangerous because important details may be obscured; in which case only partial or even incorrect theories may be achievable. In reality, the devil hides in the details.Computing as an academic subject has three main aspects: a purely mathematical one in which machines are seen as the physical realisation of formal results; a scientific one in which the emphasis is on controlled, repeatable experimentation in the pursuit of observable truth; and an engineering facet in which the delivery of cheap, fast and reliable systems is paramount. These three are often in tension with each other, but in all cases the degree to which we document our motivation and design decisions can be critical to wider acceptance of a technology. As in any scientific domain, proofs, scientific observations and engineered artefacts all need their broader context (as well as their core features) explained. Proofs and pedagogy; science and systems: this is what we should be doing.Sadly, computing is not like the established sciences in this respect. We see very few genuinely repeatable studies in the literature, and thus very few repeated experiments, making it hard to distinguish a well-founded consensus view from mere fashion. To be fair, computing does not always lend itself easily to controlled experimentation. The field sits at a nexus between mathematics, engineering, psychology and perhaps sociology. Some of our phenomena are * Corresponding author. Tel.: +44 (0) 1784 443425; fax: +44 (0) 1784 439786.

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“…We ignore all recovery productions during normal parsing, and employ backtracking to apply the recovery rules only once an error is detected. Backtracking parsers exhibit exponential behavior in the worst case [Johnstone et al 2004]. For pathological cases with repetitive backtracking, the parser is aborted, and a secondary, non-correcting, recovery technique is applied.…”

Section: Backtrackingmentioning

confidence: 99%

Natural and Flexible Error Recovery for Generated Modular Language Environments

Jonge¹,

Kats²,

Visser³

et al. 2012

ACM Trans. Program. Lang. Syst.

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Integrated development environments (IDEs) increase programmer productivity, providing rapid, interactive feedback based on the syntax and semantics of a language. Unlike conventional parsing algorithms, scannerless generalized-LR parsing supports the full set of context-free grammars, which is closed under composition, and hence can parse languages composed from separate grammar modules. To apply this algorithm in an interactive environment, this paper introduces a novel error recovery mechanism. Our approach is language-independent, and relies on automatic derivation of recovery rules from grammars. By taking layout information into consideration it can efficiently suggest natural recovery suggestions.

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Generalised Parsing: Some Costs

Cited by 18 publications

References 15 publications

Recognition is not parsing — SPPF-style parsing from cubic recognisers

Recognition is not parsing — SPPF-style parsing from cubic recognisers

Proofs and pedagogy; science and systems: The grammar tool box

Natural and Flexible Error Recovery for Generated Modular Language Environments

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