Matrix multiplication via arithmetic progressions
Journal of Symbolic Computation - Special issue on computational algebraic complexity
Context-free recognition via shortest paths computation: a version of Valiant's algorithm
Theoretical Computer Science
An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
Fast context-free grammar parsing requires fast boolean matrix multiplication
Journal of the ACM (JACM)
Introduction to Formal Language Theory
Introduction to Formal Language Theory
THE APPLICATION OF STOCHASTIC CONTEXT-FREE GRAMMARS TO FOLDING, ALIGNING AND MODELING HOMOLOGOUS RNA SEQUENCES
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Computational Linguistics
Estimation of stochastic context-free grammars and their use as language models
Computer Speech and Language
General context-free recognition in less than cubic time
Journal of Computer and System Sciences
Reducing the worst case running times of a family of RNA and CFG problems, using Valiant's approach
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Edit distance with duplications and contractions revisited
CPM'11 Proceedings of the 22nd annual conference on Combinatorial pattern matching
Parsing by matrix multiplication generalized to Boolean grammars
Theoretical Computer Science
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In this work, we present a fast stochastic context-free parsing algorithm that is based on a stochastic version of the Valiant algorithm. First, the problem of computing the string probability is reduced to a transitive closure problem. Then, the closure problem is reduced to a matrix multiplication problem of matrices of a special type. Afterwards, some fast algorithm can be used to solve the matrix multiplication problem. Preliminary experiments show that, in practice, an important time savings can be obtained.