This paper describes our submission to the CoNLL 2014 shared task on grammatical error correction using a hybrid approach, which includes both a rule-based and an SMT system augmented by a large webbased language model. Furthermore, we demonstrate that correction type estimation can be used to remove unnecessary corrections, improving precision without harming recall. Our best hybrid system achieves state-of-the-art results, ranking first on the original test set and second on the test set with alternative annotations.
In the work of REF , they propose a pipeline of the rule-based system and a phrase-based SMT system augmented by a sizeable web-based language model.
Grammatical error correction using hybrid systems and type filtering
CoNLL Shared Task
Computer Science