Grammatical Inference of PCFGs Applied to Language Modelling and Unsupervised Parsing

Scicluna, J.; De la Higuera, C.

doi:10.3233/FI-2016-1392

Artykuł - szczegóły

Tytuł artykułu

Grammatical Inference of PCFGs Applied to Language Modelling and Unsupervised Parsing

Autorzy

Scicluna J. , De la Higuera C.

Wybrane pełne teksty z tego czasopisma

https://fi.episciences.org/

Identyfikatory

DOI

10.3233/FI-2016-1392

Warianty tytułu

Języki publikacji

Abstrakty

Recently, different theoretical learning results have been found for a variety of contextfree grammar subclasses through the use of distributional learning [1]. However, these results are still not extended to probabilistic grammars. In this work, we give a practical algorithm, with some proven properties, that learns a subclass of probabilistic grammars from positive data. A minimum satisfiability solver is used to direct the search towards small grammars. Experiments on well-known context-free languages and artificial natural language grammars give positive results. Moreover, our analysis shows that the type of grammars induced by our algorithm are, in theory, capable of modelling context-free features of natural language syntax. One of our experiments shows that our algorithm can potentially outperform the state-of-the-art in unsupervised parsing on the WSJ10 corpus.

Słowa kluczowe

grammatical Inference probabilistic context free grammars minimum satisfiability congruential grammars small grammars

Wydawca

IOS Press

Czasopismo

Fundamenta Informaticae

Rocznik

2016

Tom

Vol. 146, nr 4

Strony

379--402

Opis fizyczny

Bibliogr. 93 poz., rys., tab.

Twórcy

autor

Scicluna J.

james.scicluna@univ-nantes.fr

Université de Nantes, CNRS, LINA, UMR6241 F-44000, France

autor

De la Higuera C.

cdlh@univ-nantes.fr

Université de Nantes, CNRS, LINA, UMR6241 F-44000, France

Bibliografia

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