“gnparser”: a powerful parser for scientific names based on Parsing Expression Grammar

Dmitry Y. Mozzherin; Alexander A. Myltsev; David J. Patterson

doi:10.1186/s12859-017-1663-3

“gnparser”: a powerful parser for scientific names based on Parsing Expression Grammar

BMC Bioinformatics ◽

10.1186/s12859-017-1663-3 ◽

2017 ◽

Vol 18 (1) ◽

Cited By ~ 3

Author(s):

Dmitry Y. Mozzherin ◽

Alexander A. Myltsev ◽

David J. Patterson

Keyword(s):

Parsing Expression Grammar

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BITES Instead of FIRST for Parsing Expression Grammar

Fundamenta Informaticae ◽

10.3233/fi-2011-514 ◽

2011 ◽

Vol 109 (3) ◽

pp. 323-337 ◽

Cited By ~ 1

Author(s):

Roman R. Redziejowski

Keyword(s):

Parsing Expression Grammar

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Parsing Expression Grammar and Packrat Parsing – A Review

Journal of University of Shanghai for Science and Technology ◽

10.51201/12368 ◽

2020 ◽

Vol 22 (12) ◽

Author(s):

Nikhil Mangrulkar S ◽

◽

Kavita Singh R ◽

Mukesh Raghuwanshi ◽

Shashidhar R ◽

...

Keyword(s):

Programming Language ◽

Linear Time ◽

Parsing Expression Grammar ◽

Oriented Programming Language

Bryan Ford presented Parsing Expression Grammars (PEGs) as an alternative to specify rules for programming language, along with a Packrat parser, based on an idea of memoization. The idea proposed by B. Ford guarantees parsing of grammar written using PEGs in linear time in spite of backtracking. The primary aim of the paper is to highlight the details of PEGs followed by various challenges existing for a better understanding of the readers. From the entire overview presented, it has been observed that PEGs address the issue of undesired ambiguity in grammar for computer-oriented programming language, by not allowing any ambiguity in rules itself at the first place. However, the guarantee of linear time execution comes with a cost of large heap consumption, making it infeasible to implement for large inputs. Optimizing the resources required for memoization, may allow us to utilize the benefits offered by PEGs.

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