Building a learner corpus

The paper describes a corpus of texts produced by non-native speakers of Czech. We discuss its annotation scheme, consisting of three interlinked levels to cope with a wide range of error types present in the input. Each level corrects different types of errors; links between the levels allow capturing errors in word order and complex discontinuous expressions. Errors are not only corrected, but also classified. The annotation scheme is tested on a doubly-annotated sample of approx. 10,000 words with fair inter-annotator agreement results. We also explore options of application of automated linguistic annotation tools (taggers, spell checkers and grammar checkers) on the learner text to support or even substitute manual annotation.