Grammatical Inference: Learning Syntax from Sentences

1. Front Matter

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2. Learning grammatical structure using statistical decision-trees

   • David M. Magerman

   Pages 1-21

3. Inductive inference from positive data: from heuristic to characterizing methods

   • Timo Knuutila

   Pages 22-47

4. Unions of identifiable families of languages

   • Kalvis Apsītis, Rūsiņš Freivalds, Raimonds Simanovskis, Juris Smotrovs

   Pages 48-58

5. Characteristic sets for polynomial grammatical inference

   • Colin De La Higuera

   Pages 59-71

6. Query learning of subsequential transducers

   • Juan Miguel Vilar

   Pages 72-83

7. Lexical categorization: Fitting template grammars by incremental MDL optimization

   • Michael R. Brent, Timothy A. Cartwright

   Pages 84-94

8. Selection criteria for word trigger pairs in language modeling

   • Christoph Tillmann, Hermann Ney

   Pages 95-106

9. Clustering of sequences using a minimum grammar complexity criterion

   • Ana L. N. Fred

   Pages 107-116

10. A note on grammatical inference of slender context-free languages

    • Yuji Takada, Taishin Y. Nishida

    Pages 117-125

11. Learning linear grammars from structural information

    • Jose M. Sempere, Antonio Fos

    Pages 126-133

12. Learning of context-sensitive language acceptors through regular inference and constraint induction

    • René Alquézar, Alberto Sanfeliu, Jordi Cueva

    Pages 134-145

13. Inducing constraint grammars

    • Christer Samuelsson, Pasi Tapanainen, Atro Voutilainen

    Pages 146-155

14. Introducing statistical dependencies and structural constraints in variable-length sequence models

    • Sabine Deligne, François Yvon, Frédéric Bimbot

    Pages 156-167

15. A disagreement count scheme for inference of constrained Markov networks

    • J. Gregor, M. G. Thomason

    Pages 168-178

16. Using knowledge to improve N-Gram language modelling through the MGGI methodology

    • Enrique Vidal, David Llorens

    Pages 179-190

17. Discrete sequence prediction with commented Markov models

    • Reinhard Blasig

    Pages 191-202

18. Learning k-piecewise testable languages from positive data

    • José Ruiz, Pedro García

    Pages 203-210

19. Learning code regular and code linear languages

    • J. D. Emerald, K. G. Subramanian, D. G. Thomas

    Pages 211-221

20. Incremental regular inference

    • Pierre Dupont

    Pages 222-237

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.

• Natural
• Syntax
• algorithms
• learning
• logic
• natural language
• programming

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