Classification and generation of grammatical errors

The grammatical structure of natural language shapes and defines nearly every mode of communication, especially in the digital and written form; the misuse of grammar is a common and natural nuisance, and a strategy for automatically detecting mistakes in grammatical syntax presents a challenge worth solving. This thesis research seeks to address the challenge, and in doing so, defines and implements a unique approach that combines machine-learning and statistical natural language processing techniques. Several important methods are established by this research: (1) the automated and systematic generation of grammatical errors and parallel error corpora; (2) the definition and extraction of over 150 features of a sentence; and (3) the application of various machine-learning classification algorithms on extracted feature data, in order to classify and predict the grammaticality of a sentence.