LR Parsing of Permutation Phrases
Jana Kosti\v{c}ov\'a

TL;DR
This paper introduces an efficient LR parsing algorithm for permutation phrases that significantly reduces the number of states needed, making parsing more practical for real-world applications like JSON.
Contribution
It presents a novel LR(0) automaton construction that drastically reduces state complexity for permutation phrase parsing.
Findings
State count reduced from Ω(n!) to O(2^n)
More compact parsing tables for longer permutation phrases
Effective application demonstrated on JSON parsing
Abstract
This paper presents an efficient method for LR parsing of permutation phrases. In practical cases, the proposed algorithm constructs an LR(0) automaton that requires significantly fewer states to process a permutation phrase compared to the standard construction. For most real-world grammars, the number of states is typically reduced from to , resulting in a much more compact parsing table. The state reduction increases with longer permutation phrases and a higher number of permutation phrases within the right-hand side of a rule. We demonstrate the effectiveness of this method through its application to parsing a JSON document.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNatural Language Processing Techniques · semigroups and automata theory · DNA and Biological Computing
