The world's population of non-native speakers of English is at least twice the size of the native English population. Despite the huge potential for tools to help with non-native writing, tools for automatic error detection and correction are still in their infancy. We show one particular approach that uses a large set of native data and a small set of annotated error data to detect and correct typical errors in non-native English writing.