An efficient context-free parsing algorithm

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Abstract
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Earley, Jay. “An Efficient Context-Free Parsing Algorithm”. Communications of the ACM, vol. 13, no. 2, 1970, pp. 94-102, https://doi.org/10.1145/362007.362035.
Earley, J. (1970). An efficient context-free parsing algorithm. Communications of the ACM, 13(2), 94-102. https://doi.org/10.1145/362007.362035
Earley J. An efficient context-free parsing algorithm. Communications of the ACM. 1970;13(2):94-102.
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Mathematics
Instruments and machines
Electronic computers
Computer science
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Mathematics
Instruments and machines
Electronic computers
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Description

Seeking a faster way to parse context-free grammars? This paper describes a parsing algorithm that aims to be the most efficient general context-free parsing algorithm known. It shares similarities with Knuth's LR(k) algorithm and the familiar top-down approach. It explores the algorithm’s time bounds, showing linear time on a large class of grammars, which seems to include most practical context-free programming language grammars. In an empirical comparison, the algorithm appears to outperform the top-down and bottom-up algorithms studied by Griffiths and Petrick. The evaluation details the algorithm's performance across various grammar types, highlighting its efficiency and practicality. The results suggest that the described parsing algorithm is superior to existing methods, offering a more efficient solution for parsing context-free grammars. This research benefits computer scientists and programming language developers seeking to optimize parsing performance.

This article on context-free parsing algorithms is highly relevant to Communications of the ACM, which publishes research on computer science and related areas. The paper addresses a core problem in computer science – efficient parsing – and its findings are likely to be of interest to the journal's readership, which includes researchers and practitioners in the field.

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Citations Analysis
The first research to cite this article was titled Transition network grammars for natural language analysis and was published in 1970. The most recent citation comes from a 2024 study titled Transition network grammars for natural language analysis . This article reached its peak citation in 2010 , with 22 citations.It has been cited in 152 different journals, 9% of which are open access. Among related journals, the ACM SIGPLAN Notices cited this research the most, with 23 citations. The chart below illustrates the annual citation trends for this article.
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