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Book ChapterDOI

Parsing of General Context-Free Languages

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TLDR
This chapter concentrates on three algorithms for parsing classes of context-free grammars, each of which has a time bound, which is shown to be at worst cubic in the length of the string being parsed.
Abstract
Publisher Summary One of the major advances both in the study of natural languages and in the use of newly defined languages, such as programming languages, came with the realization that one required a formal and precise mechanism for generating the infinite set of strings of a language. Both programming linguists and natural linguists independently formulated the notion of a context-free grammar as an important generative schema. This chapter focuses on this recognition problem and its related problem of “parsing,” which means to find a derivation tree of a string in the language. A variety of methods are now known for parsing classes of context-free grammars. In some sense, the crudest method is systematic trial-and-error—that is, a deterministic simulation of the nondeterministic choice of next steps in a derivation. However, such a simulation can require a number of steps, which is exponential in the length of the string being analyzed. The chapter focuses its attention on those classes of grammars that are rich enough to generate all the context-free languages. It concentrates on three algorithms for parsing classes of context-free grammars. It shows that each method parses a class of grammars sufficiently large to generate all the context-free languages. Furthermore, each method has a time bound, which is shown to be at worst cubic in the length of the string being parsed. The three methods are presented within a consistent framework and notation so that it is possible to understand both their similarities and their differences.

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Citations
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Journal ArticleDOI

An Improved Context-Free Recognizer

TL;DR: Surprisingly close connections between the Cocke-Kasami-Younger and Earley algorithms are established which reveal that the two algorithms are “almost” identical.

An efficient context-free parsing algorithm for natural languages and its applications

Masaru Tomita
TL;DR: This thesis shows that Earley's forest representation has a defect and his representation cannot be used in natural language processing, and proposes a technique to disambiguate a sentence out of the shared-packed forest representation by asking the user questions interactively.
Journal ArticleDOI

Right nulled GLR parsers

TL;DR: The right nulled generalized LR parsing algorithm is a new generalization of LR parsing which provides an elegant correction to Tomita's GLR methods and whose performance degrades gracefully to a polynomial bound in the presence of nonLR(1) rules.
Proceedings Article

The CYK approach to serial and parallel parsing

TL;DR: Traditional parsing methods for general context-free grammars have been re-investigated in order to see whether they can be adapted to a parallel processing view.
References
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Journal ArticleDOI

Gaussian elimination is not optimal

TL;DR: In this paper, Cook et al. gave an algorithm which computes the coefficients of the product of two square matrices A and B of order n with less than 4. 7 n l°g 7 arithmetical operations (all logarithms in this paper are for base 2).
Journal ArticleDOI

An efficient context-free parsing algorithm

TL;DR: In this article, a parsing algorithm which seems to be the most efficient general context-free algorithm known is described, which is similar to both Knuth's LR(k) algorithm and the familiar top-down algorithm.
Journal ArticleDOI

Recognition and parsing of context-free languages in time n3*

TL;DR: A recognition algorithm is exhibited whereby an arbitrary string over a given vocabulary can be tested for containment in a given context-free language and it is shown that it is completed in a number of steps proportional to the “cube” of the number of symbols in the tested string.
Journal ArticleDOI

General context-free recognition in less than cubic time

TL;DR: An algorithm for general context-free recognition is given that requires less than n3 time asymptotically for input strings of length n.
Proceedings ArticleDOI

Memory bounds for recognition of context-free and context-sensitive languages

TL;DR: The computational complexity of binary sequences as measured by the rapidity of their generation by multitape Turing machines is investigated and a "translational" method which escapes some of the limitations of earlier approaches leads to a refinement of the established hierarchy.