Linear complexity context-free parsing pipelines via chart constraints

Abstract

In this paper, we extend methods from Roark and Hollingshead (2008) for reducing the worst-case complexity of a context-free parsing pipeline via hard constraints derived from finite-state tagging pre-processing. Methods from our previous paper achieved quadratic worst-case complexity. We prove here that alternate methods for choosing constraints can achieve either linear or O(Nlog2N) complexity. These worst-case bounds on processing are demonstrated to be achieved without reducing the parsing accuracy, in fact in some cases improving the accuracy. The new methods achieve observed performance comparable to the previously published quadratic complexity method. Finally, we demonstrate improved performance by combining complexity bounding methods with additional high precision constraints. © 2009 Association for Computational Linguistics.