<rdf:RDF xmlns:community="http://www.bibsonomy.org/ontologies/2008/05/community#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:admin="http://webns.net/mvcb/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:cc="http://web.resource.org/cc/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:swrc="http://swrc.ontoware.org/ontology#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xml:base="http://www.bibsonomy.org/author/Horn"><owl:Ontology rdf:about=""><rdfs:comment>BibSonomy publications for /author/Horn</rdfs:comment><owl:imports rdf:resource="http://swrc.ontoware.org/ontology/portal"/></owl:Ontology><rdf:Description rdf:about="http://www.bibsonomy.org/bibtex/2fef51a34106d013137eabb1c566cfd88/sidyr"><owl:sameAs rdf:resource="http://www.bibsonomy.org/uri/bibtex/2fef51a34106d013137eabb1c566cfd88/sidyr"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#Article"/><owl:sameAs rdf:resource="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1187953/"/><swrc:date>Wed Feb 08 06:59:37 CET 2012</swrc:date><swrc:journal>Proc Natl Acad Sci U S A</swrc:journal><swrc:month>aug</swrc:month><swrc:number>33</swrc:number><swrc:pages>11629-11634</swrc:pages><swrc:title>Unsupervised learning of natural languages</swrc:title><swrc:volume>102</swrc:volume><swrc:year>2005</swrc:year><swrc:keywords>language learning model unsupervised </swrc:keywords><swrc:abstract>We address the problem, fundamental to linguistics, bioinformatics, and certain other disciplines, of using corpora of raw symbolic sequential data to infer underlying rules that govern their production. Given a corpus of strings (such as text, transcribed speech, chromosome or protein sequence data, sheet music, etc.), our unsupervised algorithm recursively distills from it hierarchically structured patterns. The adios (automatic distillation of structure) algorithm relies on a statistical method for pattern extraction and on structured generalization, two processes that have been implicated in language acquisition. It has been evaluated on artificial context-free grammars with thousands of rules, on natural languages as diverse as English and Chinese, and on protein data correlating sequence with function. 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