Learning CFG using Improved TBL algorithm

Abstract

Grammar Induction is the process of learning context free grammar from finite set of data of the positive and negative sample of language. The Problem of learning context free grammars from example has two aspects viz. determining the grammatical structure (topology) of the unknown grammar and identifying non-terminals. To solve these problems through a new hypothesis representation method called tabular representation is used , This method uses a table like data structure similar to the parse table used in the Cocke-Younger-Kasami parsing algorithm (CYK Algorithm) for context free grammar of Chomsky Normal Form. In this paper we introduce some improvement to the tabular representation algorithm (TBL) called Improved TBL algorithm (ITBL) dedicated to inference of grammar in Chomsky normal form. This ITBL algorithm uses a Genetic Algorithm (GA) to solve partitioning problem and focuses on the modified fitness function. It gives faster and optimal solutions for 5-8 string length.

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Cite this paper

@inproceedings{Bhalse2012LearningCU, title={Learning CFG using Improved TBL algorithm}, author={Nisha Bhalse and Vivek Gupta}, year={2012} }