Learnability of Pregroup Grammars

Denis Béchet 1 Annie Foret 2 Isabelle Tellier 3, *
* Corresponding author
2 LIS - Logical Information Systems
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
3 MOSTRARE - Modeling Tree Structures, Machine Learning, and Information Extraction
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : This paper investigates the learnability by positive examples in the sense of Gold of Pregroup Grammars. In a first part, Pregroup Grammars are presented and a new parsing strategy is proposed. Then, theoretical learnability and non-learnability results for subclasses of Pregroup Grammars are proved. In the last two parts, we focus on learning Pregroup Grammars from a special kind of input called feature-tagged examples. A learning algorithm based on the parsing strategy presented in the first part is given. Its validity is proved and its properties are examplified.
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Submitted on : Monday, November 26, 2007 - 10:43:56 AM
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  • HAL Id : inria-00191112, version 1

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Denis Béchet, Annie Foret, Isabelle Tellier. Learnability of Pregroup Grammars. Studia Logica, Springer Verlag (Germany), 2007, pp.225-252. ⟨inria-00191112⟩

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