Grammatical interface for even linear languages based on control sets
Information Processing Letters
Polynomial Time Learnability of Simple Deterministic Languages
Machine Learning
Learning context-free grammars from structural data in polynomial time
Theoretical Computer Science
Case-based representation and learning of pattern languages
Theoretical Computer Science - Special issue on algorithmic learning theory
Inference of Reversible Languages
Journal of the ACM (JACM)
A design principles of a weighted finite-state transducer library
Theoretical Computer Science - Special issue on implementing automata
An alternative approach to computing with words
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Stochastic Finite Learning of the Pattern Languages
Machine Learning
A formalism for representing and reasoning with linguistic information
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Machine Learning
Machine Learning
Pattern Discovery in Biosequences
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
The EMILE 4.1 Grammar Induction Toolbox
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
On Sufficient Conditions to Identify in the Limit Classes of Grammars from Polynomial Time and Data
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
A Tool for Language Learning Based on Categorial Grammars and Semantic Information
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
'NAIL': Artificial Intelligence Software for Learning Natural Language
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Stochastic k-testable Tree Languages and Applications
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Implementing Alignment-Based Learning
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
LyrebirdTM: Developing Spoken Dialog Systems Using Examples
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
A Comparison of Syntactic and Statistical Techniques for Off-Line OCR
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
What Is the Search Space of the Regular Inference?
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Finite-state transducers in language and speech processing
Computational Linguistics
Syntax-based alignment of multiple translations: extracting paraphrases and generating new sentences
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Journal of Artificial Intelligence Research
Diffusion of context and credit information in Markovian models
Journal of Artificial Intelligence Research
Identifying hierarchical structure in sequences: a linear-time algorithm
Journal of Artificial Intelligence Research
Learning context-free grammars using tabular representations
Pattern Recognition
Evolutionary induction of stochastic context free grammars
Pattern Recognition
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Learning of (context-free) grammar rules that are based on alignment between texts of a given collection of sentences has attracted the attention of many researchers. We define and study the alignment profile and formulate fuzzy similarity of alignment profiles for a given collection of sentences. Using the fuzzy-similarity-based profile alignment, we give a methodology to formulate stochastic context-free grammar (CFG) rules. We introduce profile-alignment-based dynamic sentence similarity threshold to formulate the rules of stochastic CFG. The proposed methodology is tested using Child Language Data Exchange System (CHILDES) dataset of sentences. The benefits of our approach are experimentally demonstrated. Since our approach does not make use of any domain knowledge, it is expected to be useful in wide variety of applications requiring model construction.