Generalized LR Parsing Algorithm for Grammars with One-Sided Contexts

2016 ◽  
Vol 61 (2) ◽  
pp. 581-605 ◽  
Author(s):  
Mikhail Barash ◽  
Alexander Okhotin
Keyword(s):  
Lr Parsing ◽  
Parsing Algorithm

GENERALIZED LR PARSING ALGORITHM FOR BOOLEAN GRAMMARS

2006 ◽  
Vol 17 (03) ◽  
pp. 629-664 ◽  
Author(s):  
ALEXANDER OKHOTIN
Keyword(s):  
The Other ◽  
Lr Parsing ◽  
Other Hand ◽  
Logical Connectives ◽  
Parsing Algorithm ◽  
Boolean Grammars ◽  
Mathematical Justification ◽  
Context Free ◽  
Context Free Grammars

The generalized LR parsing algorithm for context-free grammars is extended for the case of Boolean grammars, which are a generalization of the context-free grammars with logical connectives added to the formalism of rules. In addition to the standard LR operations, Shift and Reduce, the new algorithm uses a third operation called Invalidate, which reverses a previously made reduction. This operation makes the mathematical justification of the algorithm significantly different from its prototype. On the other hand, the changes in the implementation are not very substantial, and the algorithm still works in time O(n4).

scholarly journals Integration of Morphological and Syntactic Analysis based on LR Parsing Algorithm

1995 ◽  
Vol 2 (2) ◽  
pp. 59-74 ◽  
Author(s):  
Hozumi Tanaka ◽  
Takenobu Tokunaga ◽  
Michio Aizawa
Keyword(s):  
Syntactic Analysis ◽  
Lr Parsing ◽  
Parsing Algorithm

The Generalized LR Parsing Algorithm

1991 ◽  
pp. 1-16 ◽  
Author(s):  
Masaru Tomita ◽  
See-Kiong Ng
Keyword(s):  
Lr Parsing ◽  
Parsing Algorithm

Suffix languages in Lr parsing

1995 ◽  
Vol 55 (3-4) ◽  
pp. 135-153 ◽  
Author(s):  
Benjamin R. Seyfarth ◽  
Manuel E. Bermudez
Keyword(s):  
Lr Parsing

scholarly journals Recursive Neural Networks Based on PSO for Image Parsing

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Guo-Rong Cai ◽  
Shui-Li Chen
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
State Of The Art ◽  
Pso Algorithm ◽  
Training Algorithm ◽  
Image Parsing ◽  
Swarm Optimization ◽  
Recursive Neural Networks ◽  
Parsing Algorithm

This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs). State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.

scholarly journals Finding Optimal 1-Endpoint-Crossing Trees

2013 ◽  
Vol 1 ◽  
pp. 13-24 ◽  
Author(s):  
Emily Pitler ◽  
Sampath Kannan ◽  
Mitchell Marcus
Keyword(s):  
Natural Language ◽  
Common Vertex ◽  
Dependency Parsing ◽  
Dependency Tree ◽  
Parsing Algorithm

Dependency parsing algorithms capable of producing the types of crossing dependencies seen in natural language sentences have traditionally been orders of magnitude slower than algorithms for projective trees. For 95.8–99.8% of dependency parses in various natural language treebanks, whenever an edge is crossed, the edges that cross it all have a common vertex. The optimal dependency tree that satisfies this 1-Endpoint-Crossing property can be found with an O( n4) parsing algorithm that recursively combines forests over intervals with one exterior point. 1-Endpoint-Crossing trees also have natural connections to linguistics and another class of graphs that has been studied in NLP.

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