Paper Info
Title
A fast method for statistical grammar induction
Abstract
The statistical induction of stochastic context free grammars from bracketed corpora with the Inside Outside Algorithm is an appealing method for grammar learning, but the computational complexity of this algorithm has made it impossible to generate a large scale grammar. Researchers from natural language processing and speech recognition have suggested various methods to reduce the computational complexity and, at the same time, guide the learning algorithm towards a solution by, for example, placing constraints on the grammar. We suggest a method that strongly reduces that computational cost of the algorithm without placing constraints on the grammar. This method can in principle be combined with any of the constraints on grammars that have been suggested in earlier studies. We show that it is feasible to achieve results equivalent to earlier research, but with much lower computational effort. After creating a small grammar, the grammar is incrementally increased while rules that have become obsolete are removed at the same time. We explain the modifications to the algorithm, give results of experiments and compare these to results reported in other publications.
Year
DOI
Venue
1998
10.1017/S1351324998001983
Natural Language Engineering
Keywords
Field
DocType
free grammar,small grammar,large scale grammar,statistical grammar induction,fast method,computational effort,grammar learning,results equivalent,computational cost,various method,appealing method,computational complexity,grammar induction
Stochastic context-free grammar,Attribute grammar,ID/LP grammar,Computer science,Operator-precedence grammar,Synchronous context-free grammar,Adaptive grammar,Artificial intelligence,Natural language processing,Stochastic grammar,Mildly context-sensitive grammar formalism
Journal
Volume
Issue
Citations 
4
3
3
PageRank 
References 
Authors
0.68
7
2
Name
Order
Citations
PageRank
Wide R. Hogenhout182.31
yuji matsumoto23008300.05