Extracting finite structure from infinite language

Date
2005-08-01
Authors
Hopgood, Adrian A.
McQueen, T. A.
Allen, T. J.
Tepper, J. A.
Journal Title
Journal ISSN
ISSN
0950-7051
Volume Title
Publisher
Elsevier
Peer reviewed
Abstract
Description
This paper presents a novel unsupervised neural network model for learning the finite-state properties of an input language from a set of positive examples. The model is demonstrated to learn the Reber grammar perfectly from a randomly generated training set and to generalize to sequences beyond the length of those found in the training set. Crucially, it does not require negative examples. 30% of the tests yielded perfect grammar recognizers, compared with only 2% reported by other authors for simple recurrent networks. The paper was initially presented at AI-2004 conference where it won the Best Technical Paper award.
Keywords
RAE 2008, UoA 23 Computer Science and Informatics, artificial neural networks, grammar induction, natural language processing, self-organizing map, STORM (Spatio Temporal Self-Organizing Recurrent Map)
Citation
McQueen, T. et al. (2005) Extracting finite structure from infinite language. Knowledge-Based Systems, 18(4-5), pp. 135-141.
Research Institute