Learn More
Several methods are discussed that construct a nite automaton given a context-free grammar , including both methods that lead to subsets and those that lead to supersets of the original context-free language. Some of these methods of regular approximation are new, and some others are presented here in a more reened form with respect to existing literature.(More)
A new upper bound is presented for the computational complexity of the parsing problem for TAGs, under the constraint that input is read from left to right in such a way that errors in the input are observed as soon as possible, which is called the "correct-prefix property." The former upper bound, O(n9), is now improved to O(n6), which is the same as that(More)
We present an algorithm for approximating context-free languages with regular languages. The algorithm is based on a simple transformation that applies to any context-free grammar and guarantees that the result can be compiled into a finite automaton. The resulting grammar contains at most one new nonterminal for any nonterminal symbol of the input grammar.(More)
We show how techniques known from gen-erMized LR parsing can be applied to left-corner parsing. The ~esulting parsing algorithm for context-free grammars has some advantages over generalized LR parsing: the sizes and generation times of the parsers are smaller, the produced output is more compact, and the basic parsing technique can more easily be adapted(More)