This book is a useful survey of the current state of the art in automated grammatical error detection in (mostly) non-native English, by some of the leading researchers in the field, aimed at audiences in computational linguistics and computer-aided language learning. The book begins with a working definition of grammatical error, distinguishing these from typos and some kinds of spelling and punctuation errors, and points out that whereas (only) 400 million people have some kind of English as their first language, another billion speak it as their second (or are trying to learn it as such). Chapter 2 presents a brief historical overview of the field. Until the advent of statistical methods in the 1990s, most systems used some kind of rule-based parsing supplemented with mechanisms for dealing with ill-formed input. These mechanisms included pattern matching, the addition of special "mal-rules" for spotting frequently occurring forms of error, or different types of "parse fitting" and "relaxation" techiques (e.g., in the case of unification grammars, allowing for some types of unification failure). Probably the most successful of these, both in commercial terms and in terms of performance, was the Epistle system originating from IBM and subsequently included in Microsoft Word, although its primary purpose was first-language rather than secondlanguage correction. In Chapter 3 the authors explore the problems faced by second-language learners. On the basis of evidence from the Cambridge University Press corpus of learner English, it seems that the most frequent error type is simply an incorrect choice of content word, followed in turn by incorrect preposition, and incorrect determiner choice. Like many collocation errors, it is difficult to use the techniques described in the book to address incorrect content word choice, because this usually does not result in ungrammaticality, and the state of the art is not such that semantic anomaly can be reliably detected. Many systems therefore have concentrated on preposition and determiner errors.
Recently, the state-of-the-art research on GED has been surveyed by REF .