Detecting learner errors in the choice of content words using compositional distributional semantics
COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers
h International Conference on Computational Linguistics
Association for Computational Linguistics
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Kochmar, E., & Briscoe, E. (2014). Detecting learner errors in the choice of content words using compositional distributional semantics. COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers, 1740-1751. http://aclweb.org/anthology/C/C14/C14-1164.pdf
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.
We are grateful to Cambridge English Language Assessment and Cambridge University Press for supporting this research and for granting us access to the CLC for research purposes.
External link: http://aclweb.org/anthology/C/C14/C14-1164.pdf
This record's URL: https://www.repository.cam.ac.uk/handle/1810/267159