‘Calling on the classical phone’: a distributional model of adjective-noun errors in learners’ English
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Peer-reviewed
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Authors
Herbelot, A
Kochmar, E
Abstract
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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Journal Title
Proceedings of COLING 2016
Conference Name
26th International Conference on Computational Linguistics
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Technical Papers
Publisher
Association of Computational Linguistics
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Sponsorship
Ekaterina Kochmar’s research is supported by Cambridge English Language Assessment via the ALTA Institute. Aurélie Herbelot’s contribution to this paper was similarly supported by ALTA.