Manual error annotation of learner corpora is time-consuming and error-prone, whereas existing automatic techniques cannot reliably detect and correct all types of error. This paper shows that the two methods can successfully complement each other: automatic detection and partial correction of trivial errors relieves the human annotator from the laborious task of incessantly marking up oft-committed mistakes and enables him or her to focus on errors which cannot or cannot yet be handled mechanically, thus enabling more consistent annotation with considerably less manual time and effort expended.
Email your librarian or administrator to recommend adding this journal to your organisation's collection.
* Views captured on Cambridge Core between September 2016 - 23rd May 2017. This data will be updated every 24 hours.