Unsupervised Grammar Induction in a Framework of Information Compression by Multiple Alignment, Unification and Search
Abstract
This paper describes a novel approach to grammar induction that has been developed within a framework designed to integrate learning with other aspects of computing, AI, mathematics and logic. This framework, called information compression by multiple alignment, unification and search (ICMAUS), is founded on principles of Minimum Length Encoding pioneered by Solomonoff and others. Most of the paper describes SP70, a computer model of the ICMAUS framework that incorporates processes for unsupervised learning of grammars. An example is presented to show how the model can infer a plausible grammar from appropriate input. Limitations of the current model and how they may be overcome are briefly discussed.
Cite
Text
Wolff. "Unsupervised Grammar Induction in a Framework of Information Compression by Multiple Alignment, Unification and Search." European Conference on Machine Learning, 2003.Markdown
[Wolff. "Unsupervised Grammar Induction in a Framework of Information Compression by Multiple Alignment, Unification and Search." European Conference on Machine Learning, 2003.](https://mlanthology.org/ecmlpkdd/2003/wolff2003ecml-unsupervised/)BibTeX
@inproceedings{wolff2003ecml-unsupervised,
title = {{Unsupervised Grammar Induction in a Framework of Information Compression by Multiple Alignment, Unification and Search}},
author = {Wolff, J. Gerard},
booktitle = {European Conference on Machine Learning},
year = {2003},
pages = {113-124},
url = {https://mlanthology.org/ecmlpkdd/2003/wolff2003ecml-unsupervised/}
}