Paper: Hopfield Models As Nondeterministic Finite-State Machines

ACL ID C92-1021
Title Hopfield Models As Nondeterministic Finite-State Machines
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1992
Authors

Tbe use of neural networks for integrated linguistic analysis may be profitable. This paper presents the first results of our research on that subject: a Hop- field model for syntactical analysis. We construct a neural network as an implementation of a bounded push-down automaton, which can accept context-free languages with limited center-embedding. The net- work's behavior can be predicted a priori, so the pre- sented theory can be tested. The operation of the network as an implementation of the acceptor is prov- ably correct. Furthermore we found a solution to the problem of spurious states in Hopfield models: we use them as dynamically constructed representations of sets of states of the implemented acceptor. The so-called neural-network aceeptor we propose, is fast but large.