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Inducing Grammar from Sparse Data Sets

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Title: Inducing Grammar from Sparse Data Sets
Author: Khan, Labeeb
Department: School of Computer Science
Program: Computer Science
Advisor: Kremer, StefanAntonie, Luiza
Abstract: This thesis proposes new tools and methods for identifying a regular language based on a finite set of example strings. There have been a number of different approaches proposed for grammar induction. Current state-of-the-art solutions leverage a depth-first search strategy, which is an evidence based state merging strategy or a breadth-first search strategy. We propose two solutions. The first uses a simulation-based approach that samples the set of deterministic finite state automata that represent the example strings and can recognize the language generated by an unknown grammar. The second is a genetic algorithm approach that generates and evolves a population of automata to learn the example strings labelled by an unknown grammar.
Date: 2020-09
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