The augmented predictive analyzer for context-free languages—its relative efficiency

It has been proven by Greibach that for a given context-free grammar G, a standard-form grammar Gs, can be constructed, which generates the same language as is generated by G and whose rules are all of the form Z→ cY1 ··· Ym (m ≥ 0) where Z and Yi are intermediate symbols and c a terminal symbol. Since the predictive analyzer at Harvard uses a standard-form grammar, it can accept the language of any context-free Grammar G, given an equivalent standard-form grammar Gs. The structural descriptions SD(Gs, &khgr;) assigned to a given sentence &khgr; by the predictive analyzer, however, are usually different from the structural descriptions SD(G, &khgr;) assigned to the same sentence by the original context-free grammar G from which Gs is derived. In Section 1, an algorithm, originally due to Abbott is described, which converts a given context-free grammar into an augmented standard-form grammar each of whose rules is in standard form, supplemented by additional information describing its derivation from the original context-free grammar. A technique for performing the SD(Gs, &khgr;) to SD(G, &khgr;) transformation effectively is also described. In Section 2, the augmented predictive analyzer as a parsing algorithm for arbitrary context-free languages is compared with two other parsing algorithms: a selective top-to-bottom algorithm similar to Irons' “error correcting parse algorithm” and

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