IEEE Transactions on Pattern Analysis and Machine Intelligence
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
Generating Test Data with Enhanced Context-Free Grammars
IEEE Software
Statistical properties of probabilistic context-free grammars
Computational Linguistics
Conditions on consistency of probabilistic Tree Adjoining Grammars
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Precise n-gram probabilities from stochastic context-free grammars
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Model Checking Probabilistic Pushdown Automata
LICS '04 Proceedings of the 19th Annual IEEE Symposium on Logic in Computer Science
Applying Probability Measures to Abstract Languages
IEEE Transactions on Computers
Solution of an Open Problem on Probabilistic Grammars
IEEE Transactions on Computers
Recursive markov chains, stochastic grammars, and monotone systems of nonlinear equations
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
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Computational linguistics play an important role in modeling various applications. Stochastic context-free grammars (SCFGs), for instance, are widely used in compiler testing, natural language processing (NLP), speech recognition and bioinformatics. The commonality of the former projects is that all require consistent SCFGs. This article addresses the consistency problem of SCFGs. We introduce a criterion for deciding the consistency and present a method for turning an inconsistent SCFG into consistent. The feasibility of our theory is demonstrated on random test data generation for some programming languages and formal notations.