Parsing with probabilistic strictly locally testable tree languages

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dc.contributorReconocimiento de Formas e Inteligencia Artificialen
dc.contributor.authorVerdú Mas, José Luis-
dc.contributor.authorCarrasco, Rafael C.-
dc.contributor.authorCalera Rubio, Jorge-
dc.contributor.otherUniversidad de Alicante. Departamento de Lenguajes y Sistemas Informáticosen
dc.identifier.citationVERDÚ MAS, José Luis; CARRASCO JIMÉNEZ, Rafael Carlos; CALERA RUBIO, Jorge. "Parsing with probabilistic strictly locally testable tree languages". IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 27, No. 7 (July 2005). ISSN 0162-8828, pp. 1040-1050en
dc.description.abstractProbabilistic k-testable models (usually known as k-gram models in the case of strings) can be easily identified from samples and allow for smoothing techniques to deal with unseen events during pattern classification. In this paper, we introduce the family of stochastic k-testable tree languages and describe how these models can approximate any stochastic rational tree language. The model is applied to the task of learning a probabilistic k-testable model from a sample of parsed sentences. In particular, a parser for a natural language grammar that incorporates smoothing is shown.en
dc.description.sponsorshipWork supported by the Spanish Comisión Interministerial de Ciencia y Tecnología through grants TIC2003-08496-C04 and TIC2003-08681-C02-01.en
dc.subjectParsing with probabilistic grammarsen
dc.subjectStochastic learningen
dc.subjectTree grammarsen
dc.subject.otherLenguajes y Sistemas Informáticosen
dc.subject.otherCiencia de la Computación e Inteligencia Artificialen
dc.titleParsing with probabilistic strictly locally testable tree languagesen
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