Parsing expression grammars: a recognition-based syntactic foundation

By Bryan Ford. ACM-SIGACT Symposium on Principles of Programming Languages, 2004

538 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Software

View Paper: https://www.semanticscholar.org/paper/623fb7b7983188ade9ea31ce94d0b846b5abb095

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Matt Gardner
2. Jonathan Berant
3. Mohit Bansal
4. Hannaneh Hajishirzi
5. Percy Liang
6. Ben Bogin
7. Luke S. Zettlemoyer
8. Ashish Sabharwal

In Software

1. Jonathan Berant
2. Matt Gardner
3. Kevin Lin
4. Mark Neumann
5. Ben Bogin

Citations in the corpus

• Grammar-based Neural Text-to-SQL Generation (2019, methodology): “We address these issues for SQL by designing shallow parsing expression grammars (Ford, 2004) that capture the minimum amount of SQL necessary to cover most of the examples in a given dataset.”
• LL(*): the foundation of the ANTLR parser generator (2011, unknown): “…Processors [Parsing] General Terms Algorithms, Languages, Theory Keywords nondeterministic parsing, backtracking, context­sensitive parsing, semantic predicates, syntactic predicates, deterministic .nite automata, augmented transition net­works, subset construction, memoization, PEG, GLR 1.”