Stochastic context-free grammars for scale-dependent intent inference

2013 ◽  
Author(s):  
Bhashyam Balaji ◽  
Mustafa Fanaswala ◽  
Vikram Krishnamurthy
Keyword(s):  
Stochastic Context Free Grammars ◽  
Context Free ◽  
Context Free Grammars

scholarly journals Structure detection and segmentation of documents using 2D stochastic context-free grammars

2015 ◽  
Vol 150 ◽  
pp. 147-154 ◽  
Author(s):  
Francisco Álvaro ◽  
Francisco Cruz ◽  
Joan-Andreu Sánchez ◽  
Oriol Ramos Terrades ◽  
José-Miguel Benedí
Keyword(s):  
Structure Detection ◽  
Stochastic Context Free Grammars ◽  
Context Free ◽  
Context Free Grammars

RNA MODELING BY COMBINING STOCHASTIC CONTEXT-FREE GRAMMARS AND n-GRAM MODELS

2002 ◽  
Vol 16 (03) ◽  
pp. 309-315 ◽  
Author(s):  
ISMAEL SALVADOR ◽  
JOSÉ-MIGUEL BENEDÍ
Keyword(s):  
Rna Sequences ◽  
Global Relation ◽  
Stochastic Version ◽  
Pairwise Correlations ◽  
N Gram ◽  
Stochastic Context Free Grammars ◽  
Context Free ◽  
Context Free Grammars

The RNA sentences present structured regions caused by pairwise correlations, and nonstructured regions where any global relation can be found. In this paper, we present a combination of stochastic context-free grammars (SCFG) and bigram models. The SCFGs are used to represent the long-term relations of the structured part of RNA sequences, while the bigram models are used to capture the local relations of the nonstructured part. A stochastic version of Sakakibara's algorithm is used to study the SCFGs. Finally, experiments to evaluate the behavior of this proposal were carried out.

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