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

Computer Science – Artificial Intelligence

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

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.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Unsupervised Grammar Induction in a Framework of Information Compression by Multiple Alignment, Unification and Search does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Unsupervised Grammar Induction in a Framework of Information Compression by Multiple Alignment, Unification and Search, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Unsupervised Grammar Induction in a Framework of Information Compression by Multiple Alignment, Unification and Search will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-518835

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.