Unsupervised Grammar Induction in a Framework of Information Compression by Multiple Alignment, Unification and Search

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dc.creatorWolff, J Gerard
dc.date2003-11-27
dc.date.accessioned2026-07-07T03:20:38Z
dc.date.available2026-07-07T03:20:38Z
dc.descriptionThis 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.
dc.identifierhttps://arxiv.org/abs/cs/0311045
dc.identifierhttp://arxiv.org/abs/cs/0311045
dc.identifierProceedings of the Workshop and Tutorial on Learning Context-Free Grammars (in association with the 14th European Conference on Machine Learning and the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2003), September 2003, Cavtat-Dubrovnik, Croata), editors: C. de la Higuera and P. Adriaans and M. van Zaanen and J. Oncina, pp 113-124
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31897
dc.subjectArtificial Intelligence
dc.subjectI.2.6
dc.titleUnsupervised Grammar Induction in a Framework of Information Compression by Multiple Alignment, Unification and Search
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