`Calling on the classical phone': a distributional model of adjective-noun errors in learners' English
In this paper we discuss three key points related to error detection (ED) in learners{'} English. We focus on content word ED as one of the most challenging tasks in this area, illustrating our claims on adjective{--}noun (AN) combinations. In particular, we (1) investigate the role of context in accurately capturing semantic anomalies and implement a system based on distributional topic coherence, which achieves state-of-the-art accuracy on a standard test set; (2) thoroughly investigate our system{'}s performance across individual adjective classes, concluding that a class-dependent approach is beneficial to the task; (3) discuss the data size bottleneck in this area, and highlight the challenges of automatic error generation for content words.
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