On the Automatic Analysis of Learner Language: Introduction to the Special Issue

Natural language processing (NLP) has long been used to automatically analyze language produced by language learners, typically aimed at providing individualized feedback and learner modeling in intelligent computer-assisted language learning systems (see Heift & Schulze, 2007). While much interesting research has been reported, it is difficult to determine the state of the art for the automatic analysis of learner language. Which error types and other learner language properties can be detected and diagnosed automatically? How reliably can this be done, for which kind of learner language, resulting from which types of tasks? For sustainable progress on the automatic analysis of learner language it is arguably crucial to answer these questions, to discuss and compare the performance of different analysis methods on real-life learner data sets.