A chart re-estimation algorithm for a probabilistic recursive transition network

Abstract

A Probabilistic Recursive Transition Network is an elevated version of a Recursive Transition Network used to model and process context-free languages in stochastic parameters. We present a re-estimation algorithm for training probabilistic parameters, and show how efficiently it can be implemented using charts. The complexity of the Outside algorithm we present is O(N(4)G(3)) where N is the input size and G is the number of states. This complexity cart be significantly overcome when the redundant computations are avoided. Experiments on the Penn tree corpus show that re-estimation can be done more efficiently with charts.