Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 143 tok/s
Gemini 2.5 Pro 49 tok/s Pro
GPT-5 Medium 29 tok/s Pro
GPT-5 High 33 tok/s Pro
GPT-4o 85 tok/s Pro
Kimi K2 185 tok/s Pro
GPT OSS 120B 433 tok/s Pro
Claude Sonnet 4.5 37 tok/s Pro
2000 character limit reached

Relational parsing: a clean, fast parsing strategy for all context-free languages (1902.06591v1)

Published 18 Feb 2019 in cs.FL

Abstract: We present a novel parsing algorithm for all context-free languages, based on computing the relation between configurations and reaching transitions in a recursive transition network. Parsing complexity w.r.t. input length matches the state of the art: it is worst-case cubic, quadratic for unambiguous grammars, and linear for LR-regular ones. What distinguishes our algorithm is its clean mathematical formulation: parsing is expressed as a composition of simple operations on languages and relations, and can therefore be implemented using only immutable data structures. With a proper choice of these structures, a vast majority of operations performed during parsing typical programming languages can be memoized, which allows our proof-of-concept implementation to outperform common generalized parsing algorithms, in some cases by orders of magnitude.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.