State of the Art on Grammatical Inference Using Evolutionary Method

Multilingual text data are increasing day by day, which creates opportunities and challenges in the field of computational linguistics. State of the Art on Grammatical Inference Using Evolutionary Method enriches the domains of language processing with improved performance. This book is a resource that presents an approach for the grammatical inference (GI) using evolutionary algorithms. Grammatical inference deals with the standard learning procedure to acquire grammars based on the evidence about the language. It has been extensively studied due to its high importance in the various fields of engineering and science. Typically, GI as a research domain uses the unsupervised learning procedure for extracting grammatical description from a set of corpus or textual data. Several proposed approaches to GI have faced various challenges; as such, researchers have been motivated to develop a more effective approach for grammatical inference.

The prime purpose of this book is to enhance the current state of the art of grammatical inference method and present new evolutionary algorithms-based approaches for context free grammar induction. The focus is on the development of robust genetic algorithms for context free grammar induction. The key issue with any evolutionary method is handling premature convergence - a situation when diversity of population gets decreased over generations. The new algorithms discussed in this book incorporate Boolean-based operators during offspring generation within the execution of the genetic algorithm. Hence, the user has no limitation on utilizing the evolutionary methods for the grammatical inference.