Practical arbitrary lookahead LR parsing

A unifying model for lookahead LR parsing

LR - parsing

LR-parsing Kyung-Goo Doh, Hyunha Kim, David A. Schmidt 1 Hanyang University, Ansan, South Korea 2 Kansas State University, Manhattan, Kansas, USA Abstract. We combine LR(k)-parsing technology and data-flow analysis to analyze, in advance of execution, the documents generated dynamically by a program. Based on the document language’s context-free reference grammar and the program’s control struc...

An Efficient Algorithm To Induce Minimum Average Lookahead Grammars For Incremental LR Parsing

We define a new learning task, minimum average lookahead grammar induction, with strong potential implications for incremental parsing in NLP and cognitive models. Our thesis is that a suitable learning bias for grammar induction is to minimize the degree of lookahead required, on the underlying tenet that language evolution drove grammars to be efficiently parsable in incremental fashion. The ...

LR-Parsing Derived

The LR(k)-parsing algorithm is derived, i.e., presented and proved as an interplay between program development and parsing theory. The program development uses invariants and the new concept of weakest angelic precondition. The parsing theory involved relates rightmost derivability to three other transitive relations on strings. The usual stack of item sets and the finite automaton appear in an...

LR Parsing for LCFRS

LR parsing is a popular parsing strategy for variants of Context-Free Grammar (CFG). It has also been used for mildly context-sensitive formalisms, such as Tree-Adjoining Grammar. In this paper, we present the first LRstyle parsing algorithm for Linear ContextFree Rewriting Systems (LCFRS), a mildly context-sensitive extension of CFG which has received considerable attention in the last years.