Grammatical Inference: Learning Syntax from Sentences

Miclet, Laurent & Colin De La Higuera (eds.)

ISBN 10: 3540617787 / ISBN 13: 9783540617785

Editore: Springer, Berlin, 1996

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Third international colloquium, Montpellier, 1996. Still in shrinkwrap.

Codice articolo 2604

Dati bibliografici

• Titolo: Grammatical Inference: Learning Syntax from Sentences
• Editore: Springer, Berlin
• Data di pubblicazione: 1996
• Lingua: Inglese
• ISBN 10: 3540617787
• ISBN 13: 9783540617785
• Rilegatura: Softcover
• Condizione: New
• Cataloghi dei venditori:
  • All
  • Science/Technology
  • Computer

Descrizione articolo

Riassunto

This book constitutes the refereed proceedings of the Third International Colloquium on Grammatical Inference, ICGI-96, held in Montpellier, France, in September 1996.
The 25 revised full papers contained in the book together with two invited key papers by Magerman and Knuutila were carefully selected for presentation at the conference. The papers are organized in sections on algebraic methods and algorithms, natural language and pattern recognition, inference and stochastic models, incremental methods and inductive logic programming, and operational issues.

Contenuti

Learning grammatical structure using statistical decision-trees.- Inductive inference from positive data: from heuristic to characterizing methods.- Unions of identifiable families of languages.- Characteristic sets for polynomial grammatical inference.- Query learning of subsequential transducers.- Lexical categorization: Fitting template grammars by incremental MDL optimization.- Selection criteria for word trigger pairs in language modeling.- Clustering of sequences using a minimum grammar complexity criterion.- A note on grammatical inference of slender context-free languages.- Learning linear grammars from structural information.- Learning of context-sensitive language acceptors through regular inference and constraint induction.- Inducing constraint grammars.- Introducing statistical dependencies and structural constraints in variable-length sequence models.- A disagreement count scheme for inference of constrained Markov networks.- Using knowledge to improve N-Gram language modelling through the MGGI methodology.- Discrete sequence prediction with commented Markov models.- Learning k-piecewise testable languages from positive data.- Learning code regular and code linear languages.- Incremental regular inference.- An incremental interactive algorithm for regular grammar inference.- Inductive logic programming for discrete event systems.- Stochastic simple recurrent neural networks.- Inferring stochastic regular grammars with recurrent neural networks.- Maximum mutual information and conditional maximum likelihood estimations of stochastic regular syntax-directed translation schemes.- Grammatical inference using Tabu Search.- Using domain information during the learning of a subsequential transducer.- Identification of DFA: Data-dependent versus data-independent algorithms.