Exploiting a learner corpus for the development of a CALL environment for learning Spanish collocations

This paper provides an insight into ongoing research focusing on the exploitation of data from learner corpus in order to enhance the performance of an automatic tool aimed at the correction of collocation errors of L2 Spanish speakers. The procedure adopted for collocation annotation is described together with the main difficulties involved in the annotation task, such as the problem of distinguishing collocations from other kinds of idiomatic expressions and from free combinations, the problem of correction judgment, and the problem of assigning concrete error types. It is shown that the fine-grained typology used in the course of error annotation sheds lights on certain collocation error types that are generally not taken into account by automatic error correction tools, such as errors concerning the base of the collocation, target language non-words, and grammatical collocation errors.