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Collocations play a significant role in second language acquisition. In order to be able to offer efficient support to learners, an NLP-based CALL environment for learning collocations should be based on a representative collocation error annotated learner corpus. However, so far, no theoretically-motivated collocation error tag set is available. Existing(More)
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(More)
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