Procedure for quantitatively comparing the syntactic coverage of English grammars
HLT '91 Proceedings of the workshop on Speech and Natural Language
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A framework for the competitive evaluation of model inference techniques
Proceedings of the First International Workshop on Model Inference In Testing
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
Rademacher complexity and grammar induction algorithms: what it may (not) tell us
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results 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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Empirical grammatical inference systems are practical systems that learn structure from sequences, in contrast to theoretical grammatical inference systems, which prove learnability of certain classes of grammars. All current empirical grammatical inference evaluation methods are problematic, i.e. dependency on language experts, appropriateness and quality of an underlying grammar of the data, and influence of the parameters of the evaluation metrics. Here, we propose a modification of an evaluation method to reduce the ambiguity of results.