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Grammatical error correction using hybrid systems and type filtering

Published version
Peer-reviewed

Type

Conference Object

Change log

Authors

Felice, M 
Yuan, Z 
Andersen, ØE 
Yannakoudakis, Helen  ORCID logo  https://orcid.org/0000-0002-4429-7729
Kochmar, E 

Abstract

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.

Description

Keywords

Journal Title

CoNLL 2014 - 18th Conference on Computational Natural Language Learning, Proceedings of the Shared Task

Conference Name

Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task

Journal ISSN

Volume Title

Publisher

Association for Computational Linguistics
Sponsorship
[We would like to thank] Cambridge English Language Assessment, a division of Cambridge Assessment, for supporting this research.