Simulation of stochastic regular grammars through simple recurrent networks

Abstract

Formal grammars have been successfully simulated through Artificial Neural Networks. This fact has established a new approach to the problem of Grammatical Inference. First, [Pollack,91], [Giles,92] and [Watrous,92] trained network architectures from positive samples or positive and negative samples generated by regular grammars to accept or reject new strings. On the other hand, [Servan,88] and [Smith,891 used nets in which strings were fed character by character, so that the possible successors for each character were predicted. Later, [Servan,91] suggested that these networks could also predict the generation probabilities of each character in the strings generated by Stochastic Regular Grammars. Our present work shows empirical evidence supporting this suggestion.