Abstract
In this paper, convolutional neural networks are employed for English article error correction. Instead of employing features relying on human ingenuity and prior natural language processing knowledge, the words surrounding the context of the article are taken as features. Our approach could be trained both on an error annotated corpus and an error non-annotated corpus. Experiments are conducted on CoNLL-2013 data set. Our approach achieves 38.10 % in F1, and outperforms the best system (33.40 %) that participates in the task. Experimental results demonstrate the effectiveness of our proposed approach.
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Sun, C., Jin, X., Lin, L., Zhao, Y., Wang, X. (2015). Convolutional Neural Networks for Correcting English Article Errors. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_9
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DOI: https://doi.org/10.1007/978-3-319-25207-0_9
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