Local Languages, the Succesor Method, and a Step Towards a General Methodology for the Inference of Regular Grammars

Authors:
Pedro Garcia;Enrique Vidal;Francisco Casacuberta
Affiliations:
Univ. Politecnica de Valencia, Valencia, Spain;-;Univ. Politecnica de Valencia, Spain
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1987

Citing 0
Cited 10

Inference of k-Testable Languages in the Strict Sense and Application to Syntactic Pattern Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks

IEEE Transactions on Pattern Analysis and Machine Intelligence
Natural methods for robot task learning: instructive demonstrations, generalization and practice

AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Analysis and Conversion of Documents

ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Probabilistic Finite-State Machines-Part II

IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Translation with Inferred Stochastic Finite-State Transducers

Computational Linguistics
Inference of finite-state transducers from regular languages

Pattern Recognition
A wrapper approach with support vector machines for text categorization

IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Transducer inference by assembling specific languages

ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Stochastic K-TSS bi-languages for machine translation

FSMNLP '11 Proceedings of the 9th International Workshop on Finite State Methods and Natural Language Processing

Quantified Score

Hi-index	0.14

Visualization

Abstract

A methodology is proposed for the inference of regular grammars from positive samples of their languages. It is mainly based on the generative mechanism associated with local languages, which allows us to obtain arbitrary regular languages by applying morphic operators to local languages. The actual inference procedure of this methodology consists of obtaining a local language associated with the given positive sample. This procedure, which is very simple, is always the same, regardless of the problem considered, while the task-dependent features that are desired for the inferred languages, are specified through the definition of certain task-appropriate symbol renaming functions (morphisms).