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eBook details

• Title: Engineering and Computer Science Paper Abstracts (Author Abstract)
• Author : Journal of the Alabama Academy of Science
• Release Date : January 01, 2010
• Genre: Engineering,Books,Professional & Technical,
• Pages : * pages
• Size : 178 KB

Description

A STATISTICAL APPROACH TO DSL LEARNING. Marjan Mernik, Dept. of Computer and Information Science, Univ. of Maribor, Slovenia, Barrett Bryant, Alan Sprague, Thamar Solorio and Upendra Sapkota, Dept. of Computer and Information Sciences, University of Alabama at Birmingham, 35294. Grammar induction, also referred to as syntactic pattern recognition is the process of extracting syntactic structure from given sample data. Context Free Grammars (CFGs) constitute an important class of grammars which are extensively used in various applications such as description and design of programming languages and compilers and analyzing the syntax of natural languages. Grammar Induction to infer CFGs for general purpose programming is still a forbidding problem but research on inferring Domain Specific Languages (DSLs) has achieved satisfactory results. In the cases where the DSL specification has been lost and there are only DSL samples, the grammar induction can be used to generate the specifications. Applying genetic algorithms to the given samples is one approach to inferring grammars. In contrast, this work performs statistical analysis on the given samples. Similar to current unsupervised approaches for grammar induction in the field of natural language processing we perform a clustering of tokens and then infer the grammar from these clustered tokens, thereby increasing the efficiency and ease of grammar induction.

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