We describe a novel approach to error detection in adjective-noun combinations. We present and release a new dataset of annotated errors where the examples are extracted from learner texts and annotated with error types. We show how compositional distributional semantic approaches can be applied to discriminate between correct and incorrect word combinations from learner data. Finally, we show how the output of the compositional distributional semantic models can be used as features in a classifier yielding good precision and accuracy.