LARS: A learning algorithm for rewriting systems
Machine Learning
CiE '07 Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World
Limitations of current grammar induction algorithms
ACL '07 Proceedings of the 45th Annual Meeting of the ACL: Student Research Workshop
Approximation of the two-part MDL code
IEEE Transactions on Information Theory
Grammatical inference and computational linguistics
CLAGI '09 Proceedings of the EACL 2009 Workshop on Computational Linguistic Aspects of Grammatical Inference
Language structure using fuzzy similarity
IEEE Transactions on Fuzzy Systems
A bibliographical study of grammatical inference
Pattern Recognition
SSGL: a semi-supervised grammar learner
International Journal of Computer Applications in Technology
Bounding the maximal parsing performance of non-terminally separated grammars
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
A survey of grammatical inference methods for natural language learning
Artificial Intelligence Review
Formal and empirical grammatical inference
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
Using MDL for grammar induction
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Computational models of language acquisition
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
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The EMILE 4.1to olbox is intended to help researchers to analyze the grammatical structure of free text. The basic theoretical concepts behind the EMILE algorithm are expressions and contexts. The idea is that expressions of the same syntactic type can be substituted for each other in the same context. By performing a large statistical cluster analysis on the sentences of the text EMILE tries to identify traces of expressions that have this substitutionability relation. If there exists enough statistical evidence for the existence of a grammatical type EMILE creates such a type. Fundamental notions in the EMILE 4.1 algorithm are the so-called characteristic expressions and contexts. An expression of type T is characteristic for T if it only appears in a context of type T. The notion of characteristic context and expression boosts the learning capacities of the EMILE 4.1algorit hm. The EMILE algorithm is relatively scalable. It can easily analyze text up to 100,000 sentences on a workstation. The EMILE tool has been used in various domains, amongst others biomedical research [Adriaans, 2001b], identification of ontologies and semantic learning [Adriaans et al., 1993].