Parsing with probabilistic strictly locally testable tree languages

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Title: Parsing with probabilistic strictly locally testable tree languages
Authors: Verdú Mas, José Luis | Carrasco, Rafael C. | Calera Rubio, Jorge
Research Group/s: Reconocimiento de Formas e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Parsing with probabilistic grammars | Stochastic learning | Tree grammars
Knowledge Area: Lenguajes y Sistemas Informáticos | Ciencia de la Computación e Inteligencia Artificial
Issue Date: Jul-2005
Publisher: IEEE
Citation: VERDÚ 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. 1040-1050
Abstract: Probabilistic 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.
Sponsor: Work supported by the Spanish Comisión Interministerial de Ciencia y Tecnología through grants TIC2003-08496-C04 and TIC2003-08681-C02-01.
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2005.144
Language: eng
Type: info:eu-repo/semantics/article
Peer Review: si
Appears in Collections:INV - GRFIA - Artículos de Revistas
INV - TRANSDUCENS - Artículos de Revistas

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