language: en

Evolution of Recursive Transition Networks for Natural Language Recognition with Parallel Distributed Genetic Programming

Riccardo Poli
School of Computer Science The University of Birmingham
E-mail: R.Poli@cs.bham.ac.uk Technical Report: CSRP-96-19 December 1996

Abstract This paper describes the application of Parallel Distributed Genetic Programming (PDGP) to the problem of inducing programs for natural language processing. PDGP is a new form of Genetic Programming (GP) which is suitable for the development of programs with a high degree of parallelism and an efficient and effective reuse of partial results. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links representing the flow of control and results. PDGP allows the exploration of a large space of possible programs including standard tree-like programs, logic networks, neural networks, finite state automata, Recursive Transition Networks (RTNs), etc. The paper describes the representations, the operators and the interpreters used in PDGP, and illustrates its behaviour on the problem of inducing RTN-based recognisers for natural lan guage from positive and negative examples.

1 Introduction